Mining the Most Interesting Rules
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湖南省永州市第一中学2023-2024学年高一上学期第一次月考英语试题学校:___________姓名:___________班级:___________考号:___________一、阅读理解Sharon, Aged 22The most important thing to keep in mind when going into high school is to be yourself. Besides, I don’t know what your middle school was like, but high school teachers will not care about things such as how much homework you already have in one night. It’s best to just learn to deal with things and manage your time wisely so you can achieve everything you need.Frank, Aged 21I think almost every kid feels both nervous and excited before their first day. You will probably love it. I know I did. You should join in some sports or activities that will make your high school experience more enjoyable. Good luck!Eddie, Aged 20When I started high school I was really nervous too, especially since I had been home-schooled all through middle school and didn’t really know anyone, I suppose the best advice would be to just relax. The first couple of days can be a little bit hard, but things will become easier after you know it.David, Aged 19I’m not going to lie. The first day is kind of frightening. But you’ll get used to it. Don’t be afraid of anyone; upperclassmen will pick on you more if you let them know you’re afraid. Just take it easy. Making some friends and staying with them will greatly help you get used to high school quickly. After the first week it’s really not bad at all. Don’t worry.1.What can we infer from Sharon about high school?A.Teachers are quite strict.B.Students often stay up at night.C.Teachers provide little care for students.D.Students should make good use of their time.2.How did Eddie feel on his first day of high school?A.Excited B.Bored.C.Worried.D.Relaxed.3.Who mentions the importance of friends?A.Frank.B.David.C.Sharon.D.Eddie.I’m a seventeen-year-old boy preparing for my A Level exams at the end of the year. In the society where my peers (同龄人) and I live, we tend to accept the rat race values. As students, we want to get good grades so that we can get good jobs. I enjoy studying and have consistently received A’s in my classes. There was a year when I finished first in my class in the final exams. It was a great accomplishment.Another one I am pleased with is that I managed to improve the relationship between Mum and Dad. Dad was a successful businessman who was rarely at home. Mum was a housewife who always felt bored and constantly nagged (唠叨) him to let her go to work. Their constant arguing bothered me, so I advised Dad that Mum would be better off with a part-time job. He agreed, and their relationship has improved since then.My most proud achievement, however, is my successful work in the local old folks’ home. My grandparents had raised me since I was a child. I wept (哭泣) bitterly when they died. Unlike many of my classmates, I do not take part in my school’s community service to earn points. I enjoy my voluntary work and believe I’m contributing to a worthwhile cause. This is where I can help. I talk to the elderly, assist them with their daily life, and listen to their problems, glory days and the hardships they experienced.Last year, I hosted a successful New Year party for the elderly and they enjoyed a great time. Many expressed a desire to attend another party the following year. When I reflect on my accomplishments, I’m especially proud of my service at the old folks’ home, so I hope to study social work at university and work as a social worker in the future. I wish to be more skilled in attending to the less fortunate as well as find great satisfaction in it, after all. 4.What can be inferred about the author from the first paragraph?A.His good grades got him a good job.B.He refuses to compete with his peers fiercely.C.His views on social values are well known.D.He is content with his scholastic achievements.5.Which role does the author play in his parents’ relationship?A.A helper.B.A judge.C.A monitor D.A supporter. 6.What is the greatest accomplishment for the author?A.The contribution to volunteering.B.The success in exams.C.The recovery of confidence in life.D.The work in community service. 7.Why does the author want to major in social work at university?A.To gain a well-paid job.B.To give his life a purpose.C.To meet his grandparents’ expectations.D.To better help the disadvantaged."Creativity is the key to a brighter future, "say education and business experts. Here is how schools and parents can encourage this important skill in children.If Dick Drew had listened to his boss in 1925, we might not have the product that we now think of as great importance: a new type of tape. Drew worked for the Minnesota Mining and Manufacturing Company. At work he developed a kind of material strong enough to hold things together. But his boss told him not to think more about the idea. Finally, using his own time, Drew improved the tape, which now is used everywhere by many people. And his former company learned from its mistake. Now it encourages people to spend 15 percent of their working time just thinking about and developing new ideas.Creativity is not something one is just born with, nor is it necessarily a character of high intelligence. The fact that a person is highly intelligent does not mean that he uses it creatively. Creativity is the matter of using the resources one has to produce new ideas that are good for something.Unfortunately, schools have not tried to encourage creativity. With strong attention to test results and the development of reading, writing and mathematical skills, many educators give up creativity for correct answers. The result is that children can gain information but can't recognize ways to use it in new situations. They may know the rules correctly but they are unable to use them to work out practical problems.It is important to give children choices. From the earliest age, children should be allowed to make decisions and understand their results. Even if it' choosing between two food items for lunch, decision-making helps thinking skills. As children grow older, parents should try to let them decide how to use their time or spend their money. This is because the most important character of creative people is a very strong desire to find a way out of trouble. 8.What did the company where Drew once worked learn from its mistake?A.It should encourage people to work a longer time.B.People should be discouraged to think freely.C.People will do better if they spend most of their working time developing new ideas.D.It is necessary for people to spend some of their working time considering anddeveloping new ideas.9.We can know from the passage that creativity _________.A.something that most people are born withB.something that has nothing to do with intelligence at allC.a way of using what one has learned to work out new problemsD.something that is not important to the life in the future at all10.Why don't schools try to encourage creativity?A.They don't attach importance to creativity education.B.They don't want their students to make mistakes.C.They pay no attention to examination marks.D.They think it impossible to develop creativity in class.11.What should the parents do when their children decide how to spend their money?A.Allow them to have a try.B.Try to help them as much as possible.C.Take no notice of whatever they do.D.Order them to spend the least money.Livestreaming (直播) through channels such as Amazon Live and QVC is an increasingly popular way to sell goods online. It usually lasts between 5 and 10 minutes, and someone promotes a product. Viewers can then readily buy it by clicking on a link.We analyzed (分析) 99,451 sales cases on a livestream selling platform and matched them with actual sales cases. In terms of time, that is equal to over 2 million 30-second television advertisements.To determine the emotional (情绪的) expression of the salesperson, we used two deep learning models: a face model and an emotion model. The face model discovers the presence or absence of a face in a frame of a video stream. The emotion model then determines the probability that a face is exhibiting any of the six basic human emotions: happiness, sadness, surprise, anger, fear or disgust. For example, smiling signals a high probability of happiness, while an off-putting expression usually points toward anger.We wanted to see the effect of emotions expressed at different times in the sales cases sowe counted probabilities for each emotion for all 62 million frames in our database. We then combined these probabilities with other possible aspects that might drive sales — such as price and product characteristics — to judge the effect of emotion.We found that, perhaps unsurprisingly, when salespeople show more negative emotions — such as anger and disgust — the volume of sales went down. But we also found that a similar thing happened when the salespeople show high levels of positive emotions, such as happiness or surprise.A likely explanation, based on our research, is that smiling can be disgusting because it lacks true feelings and can reduce trust in the seller. A seller’s happiness may be taken as a sign that the seller is gaining interests at the customer’s expense.12.What can we know about the livestreaming to sell goods online?A.It challenges the physical economy.B.It helps big company sell all goods.C.It is very convenient for the buyers.D.It helps the sellers develop fixed expressions.13.What does the underlined word “off-putting” mean in paragraph 3?A.Unhappy.B.Embarrassed.C.Surprised.D.Shy.14.How do customers feel about the seller’s smiling?A.They feel thankful.B.They feel happy.C.They feel cheated.D.They feel so tired.15.Which of the following could be the best title for the text?A.Livestreamers Sell Products SuccessfullyB.Smiling Can Increase the Sales in RealityC.Emotions and Faces: What’s the DifferenceD.Expressions Affect Selling Products Online二、七选五Students must then answer properly, using materials they have learned in preparation for the exam. Showing plentiful knowledge of the subject results in passing the exam or an excellentgrade.17 As part of the completion of a program at the undergraduate or graduate levels, students might need to prove their knowledge and understanding of a subject area. Many science majors finish Bachelor’s studies with oral exams, or a particular program may require oral and written exams that show how a student has taken in all the materials learned in a four-year period. Usually study guides are available for these exams. 18 One valuable tip for students to succeed is to practice before the exam. 19 But by answering questions classmates or family ask them students can do as many practices as possible. On the day of the exam, students should dress plainly but respectfully, and in keeping with any dress requirements.Students taking these exams should remember how much they have learned, which is hopefully quite a lot. 20 When waiting outside the test room, taking a deep breath and reminding themselves that the oral exam is a nice way to show off how much knowledge has been learned from teachers can help overcome stressful moments. Also, any other relaxation techniques that help can be used.A.It raises questions to students in spoken form.B.Behaving properly can earn students some favor.C.They may not be able to predict all the questions.D.Usually, students performing well are experienced.E.Nervousness can even make the smartest student get stuck.F.There are many examples in college where oral exams are used.G.Therefore, prepared students tend not to be surprised by what they’re asked.三、完形填空The sun was shining brightly over our heads and sweat (汗水) was pouring off our backstraining 28 .After this journey, we were more 29 than we had imagined and we were much braver than we had 30 . Girls who looked pale rested for a little while, and then right away came back to the team. Boys who were being punished 31 to the playground at once and began to run. Sweat flowed down our faces when we thought about how to shout 32 than other classes.Military training taught us perseverance (坚持不懈) and determination. On the last day of our training, the confidence could be 33 on our faces. We shouted so loud that our 34 could be heard across the heavens. With eyes like burning torches (火炬), we walked into the future. Now the sun is 35 at all of us.21.A.crying B.making C.sending D.telling 22.A.nothing B.anything C.something D.everything 23.A.wasted B.spent C.kept D.took 24.A.strict B.curious C.interesting D.normal 25.A.success B.pleasure C.worry D.wonder 26.A.referred to B.contributed to C.turned to D.listened to 27.A.sunlight B.wealth C.desire D.health 28.A.received B.helped C.offered D.happened 29.A.concerned B.determined C.interested D.surprised 30.A.expected B.expressed C.disliked D.rescued 31.A.escaped B.moved C.rode D.rushed 32.A.larger B.stronger C.louder D.lower 33.A.seen B.heard C.smelt D.touched 34.A.thoughts B.feelings C.opinions D.voices 35.A.staring B.laughing C.waving D.smiling四、用单词的适当形式完成短文阅读下列短文,在空白处填入1个适当的单词或括号内单词的正确形式。
描述美国历史与移民政策的英语作文全文共6篇示例,供读者参考篇1American History and Immigration PoliciesHi there! Today, I'm going to talk about the history of America and how it has welcomed people from all over the world through its immigration policies. It's an exciting and important topic that has shaped the country we know today.America is often referred to as a "nation of immigrants." That's because, except for the Native Americans who were the original inhabitants of the land, everyone else came from somewhere else. Even the early settlers, like the Pilgrims who arrived on the Mayflower in 1620, were immigrants from Europe.In the beginning, America was a collection of colonies ruled by different European powers like Britain, France, and Spain. Many people came to the colonies seeking religious freedom, economic opportunities, or a fresh start in a new land. The colonies welcomed immigrants, but they had to follow the rules set by their European rulers.Things changed after the American Revolution in 1776. The thirteen colonies broke away from British rule and formed the United States of America. As a new independent nation, the United States could make its own immigration laws.At first, the new country didn't have many immigration restrictions. People from all over Europe, particularly from countries like Ireland, Germany, and Italy, came to America in search of better lives. They were attracted by the promise of economic opportunities, religious freedom, and the chance to own land.As the years went by, and more and more immigrants arrived, some people started to worry that too many foreigners were coming into the country. They feared that these newcomers might threaten American culture and values. So, in the late 1800s and early 1900s, the government started to pass laws to limit and control immigration.One of the most famous immigration laws was the Chinese Exclusion Act of 1882. This law prevented Chinese laborers from entering the United States. It was the first time the government had excluded a specific group of people based on their race or nationality.Another important law was the Immigration Act of 1924, also known as the National Origins Act. This law set quotas, or limits, on how many immigrants could come from each country. The quotas favored immigrants from Western European countries like Britain and Germany, while restricting immigration from Eastern and Southern Europe, as well as from Asia.During World War II, the United States also turned away many Jewish refugees fleeing from Nazi Germany. The government was worried that some of these refugees might be spies or criminals, so they didn't let them in. This decision was later criticized as a shameful moment in American history.After World War II, attitudes towards immigration started to change. The country needed more workers to help rebuild and grow the economy. In 1965, the Immigration and Nationality Act was passed. This law abolished the old quota system and made it easier for people from all over the world to come to America.Today, America is one of the most diverse countries in the world. People have come from every corner of the globe, bringing their cultures, traditions, and dreams with them. While immigration has sometimes been a controversial topic, it has also been a source of strength and renewal for the nation.Immigration has played a huge role in shaping American history and culture. From the early settlers to the waves of immigrants in the 19th and 20th centuries, newcomers have contributed to the country's economic growth, scientific advances, and artistic achievements.As an elementary school student, I'm proud to live in a country that has welcomed people from all over the world. I have classmates and friends whose families came from different countries, and I've learned so much from them. Their stories and traditions have enriched my understanding of the world and made me appreciate the diversity that immigration has brought to America.In the future, I hope America continues to be a beacon of hope and opportunity for people seeking a better life. While immigration policies may change over time, the spirit of welcoming and embracing newcomers should remain at the heart of what it means to be American.That's my take on American history and immigration policies. It's a complex topic, but one that has played a crucial role in shaping the country we live in today. I'm grateful for the contributions of immigrants, and I look forward to seeing howtheir stories will continue to enrich and strengthen the United States.篇2American History and ImmigrationHi there! My name is Emma and I'm going to tell you all about the history of America and how its immigration policies have changed over time. It's a pretty interesting story with lots of ups and downs.America was first explored and settled by European colonists starting in the 1600s. The earliest English settlers landed in Virginia in 1607 and established Jamestown, which was the first permanent English colony in what would later become the United States. More colonies were founded after that, including Massachusetts, Maryland, Pennsylvania, Georgia, and others.Most of the early colonists were from England, but over time immigrants started arriving from other European nations like Germany, Ireland, Scotland, and the Netherlands. The English colonies needed people to settle the land and grow crops, so they welcomed immigrants who could help build the colonies.As more and more colonists arrived, tensions grew between the colonists and the British government over taxes, representation, and independence. In 1776, the colonists declared independence from Britain and fought the Revolutionary War. After winning the war in 1783, the United States of America was officially established as an independent nation.In the early years of the United States, there were no strict immigration laws. The new country continued to allow immigrants, especially from Europe, to arrive and settle. New settlers and immigrants were needed to explore, farm, and build in the growing nation.One of the key principles of America is that it is a nation of immigrants. The United States was built by wave after wave of immigrants coming from all over the world. The ancestors of most Americans today can be traced back to immigrants who came seeking a better life.As the 1800s progressed and America continued expanding westward, immigration rates increased rapidly. Millions of immigrants poured in from Europe, especially from Ireland and Germany where there was widespread poverty and famine.Immigrants also started arriving from China to help build railroads and work in mining.With so many immigrants coming, the first immigration laws were passed in the late 1800s to start controlling the flow of people. The Chinese Exclusion Act of 1882 was the first major law restricting immigration based on nationality. It stopped immigration from China for 10 years.In the early 1900s, even more immigrants flooded in from Southern and Eastern Europe. This included many Italians, Greeks, Poles, Russians and others. To handle the large numbers, the government opened Ellis Island in 1892 as an entry point and inspection station for immigrants arriving in New York.Between 1880 and 1920, around 25 million European immigrants came to America through places like Ellis Island, seeking better opportunities. This huge wave of immigration from Europe eventually led to new restrictive laws in the 1920s limiting the numbers of immigrants allowed based on nationality.The Immigration Act of 1924 established quotas based on national origins that heavily favored immigration from Western Europe while sharply restricting arrivals from places like Italy,Greece, Poland and other parts of Eastern and Southern Europe. Very few immigrants were allowed from Asian countries.This quota system remained in place for over 40 years until it was replaced by the Immigration and Nationality Act of 1965. This new law abolished the discriminatory national origins quota system and established a new policy focused on reuniting immigrant families and attracting skilled labor to the United States.After the 1965 reforms, immigration surged again from all around the world, with large numbers of immigrants coming from Asia and Latin America in addition to Europe. Between 1965 and 2000, over 25 million legal permanent residents entered the U.S.Today, the debate over immigration reform continues. Some want to increase immigration to attract workers and talent, while others want to reduce immigration levels. There are also debates over policies regarding unauthorized or illegal immigration.Immigration has played a huge role in American history from the earliest days. The United States was founded and built by generations of immigrants seeking freedom and opportunity. While immigration policies have gone through many changes over the centuries, immigrants from all over the globe continueto come to America and become part of the nation's rich diversity.I hope you found this overview interesting! Immigration is a really important part of America's story. Even though policies have shifted, the idea of America as a nation of immigrants has remained a core part of its identity and values. Thanks for reading!篇3America: A Nation of ImmigrantsHi there! My name is Emily and I'm 10 years old. Today, I want to tell you all about the history of America and how it became a nation of immigrants.A long, long time ago, there were no cities, roads or houses in America. The only people living here were Native Americans. They lived in tribes, hunted animals, fished in rivers and lakes, and grew crops like corn, beans and squash. They had their own languages, customs and beliefs.Then in 1492, an Italian explorer named Christopher Columbus arrived on a ship from Europe. He thought he had reached India, so he called the native people "Indians." Butscientists later realized he had actually landed on islands near North America. More European explorers like John Cabot, Jacques Cartier and Juan Ponce de León followed. They claimed land for their countries and set up colonies.The first permanent English colony was Jamestown, founded in 1607 in what is now the state of Virginia. The colonists had a really hard time at first. They didn't know how to farm the land properly and didn't have enough food or shelter. A lot of them got sick or died. But they kept getting new settlers and supplies from England.In 1620, a group of English Protestants called the Pilgrims arrived in North America aboard the Mayflower ship. They created the Plymouth Colony in present-day Massachusetts. Unlike the colonists at Jamestown, the Pilgrims had a documented agreement on how they would govern themselves called the Mayflower Compact. This helped them work together.Over time, more and more European immigrants came to America. Some came voluntarily to start a new life and pursue religious freedom, while others were brought as slaves from Africa against their will. By the 1700s, there were 13 British colonies along the East Coast of North America.In 1776, leaders from these colonies declared independence from Britain in the Declaration of Independence. After winning the Revolutionary War, America became an independent nation - the United States of America! The colonists were now considered American citizens.During the 1800s, millions of immigrants continued flocking to America, many from Europe. They came by ship looking for better opportunities and life away from poverty or oppression in their home countries. Large numbers of Irish fled the potato famine, while Germans, Italians, Poles and others sought jobs and land.Not everyone was welcomed though. Laws were passed like the Chinese Exclusion Act to stop immigration from China. There were also big arguments about whether new Western territories should allow slavery.In the early 1900s, immigrants continued arriving from southern and eastern Europe. But then World War 1 happened and immigration slowed for a while. After the war, the U.S. passed national origin quotas to limit immigration from certain parts of the world.Things changed again during World War 2 when America allowed in refugees from the war. Then in 1965, a new law endednational origin quotas and opened doors to more immigrants from around the world, not just Europe.Today, America is one of the most diverse nations with people from all backgrounds, cultures and religions. Immigrants and their descendants make up a huge part of the country's population. People continue coming to America legally as students, workers, or to reunite with family members. But there's also a lot of debate about controlling illegal immigration - people entering or staying without proper documentation.Overall, America was built by generations of immigrants over hundreds of years. Each new group faced challenges and sometimes discrimination. But together they formed the rich, multicultural society of the United States that we know today.No matter where your family is originally from, if you live in America now, you're an American! This country was made possible by the hopes, dreams, hard work and sacrifices of countless immigrants searching for freedom and opportunity. That's why the words on the Statue of Liberty say "Give me your tired, your poor, your huddled masses yearning to breathe free."篇4America: A Nation of ImmigrantsDo you know that unless you are a Native American, your family came from another country? That's right, we are all immigrants or descended from immigrants who came to America from somewhere else. This great nation was built by people from all over the world. Let me tell you the story of how America became a nation of immigrants.A Long Time AgoA very long time ago, there were no countries or borders. People lived in small tribes and moved from place to place to find food and shelter. About 15,000 years ago, the ancestors of today's Native Americans began migrating across a land bridge from Asia into North America during the Ice Age. These were the very first immigrants to the continent we now call America.The Europeans ArriveFor thousands of years, Native Americans were the only people living in America. Then in 1492, an Italian explorer named Christopher Columbus arrived from Europe. He thought he had reached the Indies (Asia), so he called the native people "Indians." More European explorers soon followed, seeking wealth and claiming lands for their nations.The First English SettlersIn 1607, the first permanent English settlement was established at Jamestown, Virginia. More English colonists kept coming, along with immigrants from other European countries. Most came seeking economic opportunities or religious freedom that they could not find at home.The United States is BornBy the 1700s, there were 13 British colonies along the Atlantic coast. The colonists grew unhappy with unfair treatment by the British government. In 1776, they declared independence and fought the Revolutionary War. After winning, they formed a new nation - the United States of America.More Settlers Head WestAs the young United States expanded westward, even more immigrants poured in from across the Atlantic. Millions of Europeans came seeking cheap farmland and a fresh start, including large numbers of Irish and Germans fleeing famine and poverty. Chinese immigrants arrived to work on the Transcontinental Railroad.The New Immigration WaveIn the late 1800s, a new wave of immigrants started coming from Southern and Eastern Europe - Italians, Poles, Russians, andothers. Many were escaping religious persecution or looking for jobs in booming U.S. industries like coal mining and steelmaking. Chinatowns and Little Italys sprouted in big cities.Restrictions BeginAs immigration soared in the early 1900s, some Americans became worried about so many newcomers. They thought it was too much change happening too fast. In 1924, strict new limits were placed on immigration from Europe and other parts of the world. Only small numbers were allowed entry each year.New Sources of ImmigrationAfter World War II, immigration slowly increased again from new sources like Latin America and Asia. The U.S. economy was booming and needed more workers. In 1965, the old national quotas were abolished in favor of a new policy emphasizing family reunification and job skills.Today's ImmigrantsIn recent decades, most immigrants to the U.S. have come from Asia and Latin America, especially Mexico. Some enter legally, others come without documentation. Immigration has helped make America a diverse nation of many cultures,languages and religions living together. Love of freedom and opportunity still draws people from all over the globe.Debate Over ImmigrationThere is an ongoing debate about immigration policies in America today. Some people want to allow more immigrants to enter legally and provide a path for undocumented immigrants to become citizens. Others want to limit immigration, especially undocumented entries. They are concerned about security risks, job competition and costs of social services.Whatever your view, one thing is clear - the story of America is a story of immigration from its very beginning. This nation was built by generation after generation of immigrants seeking a better life. Their hopes and dreams, along with the cultures they brought with them, helped shape the diverse and prosperous America we know today. And the story continues...篇5The Story of America and Its ImmigrantsAmerica is a nation of immigrants. That means that except for the Native Americans who were here first, every single person in the United States has ancestors who came from somewhereelse. My great-grandparents were immigrants from Italy and Ireland. Maybe your ancestors came from Mexico, China, India, or Nigeria. We're all descendants of people who left their home countries and came to America.For as long as people have lived in North America, there has been migration and movement between lands. The Native Americans themselves migrated across the Bering Land Bridge from Asia thousands of years ago. They spread out across the continents, forming many diverse tribes and nations.Then, in the 1500s and 1600s, European explorers and settlers began arriving and creating colonies in what would become the United States. The first successful English colony was at Jamestown, Virginia in 1607. Over the next few centuries, millions more immigrants came from Europe, especially from England.Most of the earliest European immigrants came voluntarily, seeking freedom and opportunity. But there was also a dark side – the forced migration of enslaved Africans brought over in the transatlantic slave trade. This terrible injustice left a painful legacy that still impacts America today.In the 1800s, America's immigrant population became even more diverse, with large numbers arriving from Germany, Ireland,China, and other nations. Chinese immigrants helped build the Transcontinental Railroad. But they faced racism and the Chinese Exclusion Act that barred more immigration for decades.By the 1900s, there was a new great wave of immigration from Southern and Eastern Europe. Millions of Italians, Poles, Russians, and others came through Ellis Island dreaming of freedom and opportunity. But racism and discrimination made life hard for many of these new immigrants too.Finally, in 1965, America passed the Immigration and Nationality Act which did away with discriminatory quotas based on nationality and race. This opened the door to even more diversity, with immigrants coming from all across the world –Latin America, Asia, the Middle East, Africa, and everywhere else.Throughout its history, immigration has had a huge impact on America's economy, culture, and identity as a nation of different people united by shared ideals of liberty, equality, and democracy. Immigrants today continue to make America dynamic, innovative, and prosperous.But immigration has also always been controversial, with debates raging over how many immigrants to allow in and what the criteria should be. The 19th century saw backlash against Catholic immigrants, while the 20th century had arguments overrejecting Jewish refugees from the Nazis. In recent decades, the big debates have centered on immigrants from Mexico, Central America, and Muslim-majority countries.Those in favor of more immigration point to America's founding ideals welcoming immigrants, as well as the economic and cultural benefits they bring. Critics worry about immigration's impact on jobs, security, and national unity. They want more limits and border security.The immigration debate rages on today as America keeps arguing over what kind of immigration policies it should have:How many immigrants should be allowed in each year?Should there be more border security and deportations?What criteria should immigrants need to meet (job skills, English ability, etc.)?Should immigrants be admitted based more on employment/skills or reuniting families?Should there be a path to citizenship for undocumented immigrants brought as children?How should immigration policies balance economic, security, and humanitarian concerns?These are hard questions without easy answers. But immigration has always been central to America's story as a nation of immigrants constantly being reshaped and redefined by each new wave of newcomers seeking the American dream.So that's the story of America and its immigrants – a nation of people coming from all over the world, some voluntarily and some forced, all contributing to the kaleidoscope of diversity that shapes our national identity. It's a story of contradictions, of openness and resistance, of new beginnings and long struggles. And it's a story that's still being written by the immigrants of today and tomorrow.篇6America: A Nation of ImmigrantsHi there! Let me tell you a story about America and all the people who came here from faraway lands. It's a tale of dreams, hopes, and a country that was built by immigrants like your ancestors.A long time ago, even before your great-great-grandparents were born, America was a vast land inhabited by Native American tribes. They lived in harmony with nature, hunting,fishing, and cultivating crops. Their way of life was simple but fulfilling.Then, in 1492, an Italian explorer named Christopher Columbus sailed across the Atlantic Ocean and landed in the Caribbean islands. He thought he had reached the Indies (which is now called Asia), so he called the native people "Indians." Little did he know, he had discovered a whole new continent!After Columbus, many more Europeans began exploring and settling in the "New World." The first successful English colony was established in 1607 at Jamestown, Virginia. The colonists had a tough time at first, but they learned to farm and trade with the Native Americans.Over the next few centuries, millions of immigrants from all over the world came to America in search of a better life. They escaped poverty, religious persecution, and wars in their home countries. Some came as indentured servants or enslaved people, while others arrived as free settlers.One of the most significant waves of immigration happened in the 1800s, when millions of Europeans fled famine and political turmoil. The Irish Potato Famine forced many Irish families to seek refuge in America. Italians, Germans, Poles, andRussians also arrived in large numbers, often settling in big cities like New York, Boston, and Chicago.During this time, the United States government passed several laws to control and restrict immigration. The Naturalization Act of 1790 allowed only "free white persons" to become citizens. Later, laws like the Chinese Exclusion Act of 1882 and the Immigration Act of 1924 further limited immigration from certain countries.In the early 1900s, a new wave of immigrants arrived from southern and eastern Europe. Many of them were poor and faced discrimination from earlier European immigrants who had already settled in America. Despite the challenges, these newcomers worked hard, started businesses, and became an integral part of American society.After World War II, the United States opened its doors to refugees from war-torn countries and survivors of the Holocaust. The Immigration and Nationality Act of 1965 abolished the discriminatory quota system and allowed more immigrants from Asia, Africa, and Latin America.Today, America is a melting pot of cultures, religions, and traditions. People from all over the world continue to come here, seeking the American Dream – a life of freedom, opportunity,and prosperity. However, immigration policies are still hotly debated, with some favoring stricter laws and others advocating for more open borders.No matter what side you're on, one thing is clear: America would not be the great nation it is today without the contributions of immigrants. From the first settlers to the latest arrivals, immigrants have helped shape American culture, built cities, fought in wars, and made countless contributions to science, art, and technology.So the next time you see someone who looks or sounds different from you, remember that their family probably came to America searching for a better life, just like your ancestors did. We're all part of the great American story, a nation built by people from every corner of the globe.。
八年级下册英语听力部分第四单元全文共6篇示例,供读者参考篇1Unit 4 Listening - My Struggles and TriumphsEnglish class can be pretty tough sometimes, especially the listening part. In Unit 4, we had to listen to all kinds of dialogues and passages and then answer comprehension questions. It was not easy!The first dialogue was about two friends making plans to go to the movies. I thought I had it down at first - Jake wanted to see the new superhero movie and Lily preferred a romantic comedy. But then they started discussing times and locations, and I got a little lost. Did Jake say he could only go on Saturday evening or Sunday afternoon? I missed that part.The next dialogue covered shopping for gifts. A daughter was asking her mom for advice on what to buy her dad for Father's Day. Again, I caught the main idea of needing a present for dad. However, there were so many specific details about his hobbies, favorite colors, and past gifts that just went right over my head. How was I supposed to remember all that?Things didn't get any easier with the longer listening passages. One was about the history of poker and all the different variants of the game. I could barely even pronounce some of those crazy poker terms they were using, let alone understand the rules! The passage on the evolution of smartphones was slightly more interesting to me, but still loaded with technical vocabulary that had me scratching my head.I have to admit, after bombing that listening section test, I felt pretty discouraged about my English skills. Why couldn't I just understand simple conversations without missing key details? How would I ever pass the bigger tests coming up?That's when my English teacher Ms. Roberts stepped in to give me a big pep talk. She reminded me that listening comprehension is one of the most challenging skills to master in any new language. It takes a ton of practice, patience, and perseverance.Ms. Roberts said I was already making great progress from where I started at the beginning of the school year. Back then, I could barely catch a couple words here and there when we did listening exercises. Now I at least had the tools to identify the main topics and ideas. The finer details would come with more exposure over time.She also shared some great tips, like listening for key words instead of trying to catch everything. Using context clues from the parts I did understand to piece together the missing gaps. Most importantly, not getting frustrated when my mind went blank for a moment, but just keeping my ears focused and waiting for the next understandable part.With Ms. Roberts' advice and lots of hard work, I could feel myself improving little by little throughout the rest of the semester. Listening exercises that used to seem like complete gibberish gradually started making more sense. While I still had room for growth, I was no longer getting discouraged or overwhelmed.The last listening test for Unit 4 went so much better than that first one. I sailed through the dialogues about everyday situations like eating out and shopping. The passages on more complex topics like space exploration were still pretty tough, but I managed to answer most of the questions correctly this time. What a contrasting feeling of pride compared to my earlier failure!I realized that making progress in English listening is almost never a straight path. It's filled with ups and downs, easy wins and major struggles, demoralizing moments and thenbreakthroughs. Every student goes through this normal roller coaster. The key is having supportive teachers, helpful strategies, and most of all, a positive mindset that doesn't give up.I've got a long road ahead before I can call myself truly fluent in listening to English. Still, I'm going to keep putting in the work, growing my skills day by day. Because feeling that sense of understanding and accomplishment makes it all worthwhile. Maybe listening exercises won't seem so scary after all by the time I reach high school!篇2Hi there! My name is Emma and I'm in 8th grade. Today I want to tell you all about the listening section we just did in Unit 4 of our English textbook. It was actually pretty interesting!The unit was called "Going Green" and it was all about environmental issues and how we can protect the planet. The listening exercises really helped bring the topic to life.In the first exercise, we listened to a conversation between two friends talking about an environmental club at their school. One friend was trying to convince the other to join. I thought it was a realistic conversation - they used a lot of casual languageand slang like "It's gonna be so much fun!" and "Come on, don't be a party pooper!"The speaking was pretty natural too. The girl who wanted to join the club asked questions like "What kind of activities do they do?" and the other friend responded with details about beach clean-ups, recycling projects, and so on. It definitely made me want to join an environmental club if my school had one!Next up was a longer listening exercise about the problem of electronic waste or "e-waste." It was a news-style report explaining what e-waste is, why it's an issue, and some potential solutions. I have to admit, some of the vocabulary was pretty challenging for me at first - words like "hazardous," "contaminants," and "disposal."But the reporter spoke clearly and the report had a good structure with an introduction, examples in the middle, and a conclusion summarizing the main points at the end. Our teacher also had us answer comprehension questions along the way, which really forced me to pay close attention.By the end, I realizedI had learned a ton of new information! Like, did you know that things like old phones, computers, and televisions can leak toxic chemicals into the environment if they aren't disposed of properly? And some of the practices theymentioned for dealing with e-waste, like refurbishing and recycling components, were really interesting to me.Who knew listening exercises could actually be so educational and eye-opening? I'll definitely think twice before just tossing out any old electronics from now on.The last listening was probably my favorite though - it was a fun song all about reducing, reusing, and recycling. The lyrics were kind of silly but they made all the main points stick in my head SO well. Like the line "Don't be a waste maker, be are-creator!" How brilliant is that?I already find myself singing little lines from the chorus when I'm trying to decide if I can reuse something instead of just throwing it away. The song-writers were geniuses to turn environmental tips into such a catchy, memorable tune.Overall, while listening exercises can sometimes feel like a chore, this unit really opened my eyes to some importantreal-world issues in a way that kept me engaged and interested the whole time. The variety of conversations, reports, and even a song made the experience way more dynamic than just reading facts off a page.I came away understanding way more about e-waste, recycling, and how small actions to reduce waste can have a big impact. Who knew learning English could also make me want to be a better environmental steward?I'm actually really excited to see what sorts of creative, informative listening exercises are coming up in the next units. Maybe I'll even write one myself one day! For now, I've got plenty of new terms and concepts from this unit to practice applying in my speaking and writing. Getting to improve my English skills while learning about important issues? Now that's what I call a win-win!篇3Unit 4 - Going GreenHi there! My name is Emma and I'm a 4th grader at Oakwood Elementary School. For our English listening practice this unit, we learned all about going green and protecting the environment. It was really interesting and I wanted to share some of the things I learned with you!First up, we talked about what it actually means to "go green." Basically, it refers to making choices and taking actions that are better for the planet. This could be things like recycling,saving energy and water, or reducing waste and pollution. The main idea is trying to live in a more environmentally-friendly or sustainable way.Our teacher showed us some pretty mind-blowing facts about why going green is so important these days. Did you know that the Earth's temperature has risen by around 1°C in just the last 100 years or so? That might not sound like much, but even small temperature changes can cause huge problems like rising sea levels, more extreme weather, loss of plant and animal species, and other scary stuff. A big reason for this is human activities that release greenhouse gases into the atmosphere and contribute to climate change.We also learned that we're losing forests at an alarming rate due to deforestation for things like agriculture, mining, and urban development. It's estimated that 18 million acres of forest are cut down each year - that's an area larger than the country of Portugal! This is really bad for the environment since trees absorb carbon dioxide and produce oxygen. Plus, millions of plants and animals lose their natural habitats.Another major issue is excessive waste ending up in landfills and the ocean. The statistics on plastic pollution in particular are just staggering. Something like 8 million metric tons of plasticwaste enters the oceans every year! That's the equivalent of dumping a garbage truck full of plastic into the water every single minute. So sad to think of all the marine life getting tangled up in or ing篇4My Struggle with Unit 4 ListeningHey everyone, it's me again - Jack from 8th grade. I'm here to give you all the juicy details about the listening struggle I've been having with Unit 4 in our English textbook. Get ready, because this is going to be a wild ride!First off, let me set the scene. Unit 4 is all about talking about hobbies and free time activities. Sounds pretty straightforward, right? Well, little did I know, it was going to be a massive headache thanks to those dreadful listening exercises.It all started with Exercise A, where we had to listen to a bunch of kids chatting about what they like to do for fun. Simple enough, or so I thought. But whenever I tried to tune in, it was like my brain went into screensaver mode. I could make out a few words here and there - "soccer", "video games", "shopping" - but piecing together whole sentences? Forget about it.My friend Samantha, being the grade-A student that she is, seemed to breeze through that first exercise. I tried to convince her to share her secrets, but all she said was, "Jack, you just have to pay closer attention!" Gee, thanks for that brilliant advice, Sam.Moving on to Exercise B, where we were supposed to listen for specific details about these kids' hobbies. I might as well have been listening to gibberish at this point. Anytime a new voice came on, I lost the thread completely. How was I meant to catch the part about how often this imaginary kid played basketball? I don't have bionic ears!And don't even get me started on Exercise C - the one with those terrible-quality recordings that sounded like they were made in a broken tin can. Were those accents British? Australian? Martian? Who knows! All I heard were vague mumbles about "watching movies" and "going cycling". My brain checked out approximately 30 seconds in.At that point, I was ready to give up and become a monk who takes a vow of silence. This listening stuff was just not my strong suit. But then I remembered - I'm Jack, and I never back down from a challenge!So I buckled down and started coming up with strategies. First up, I convinced my dad to makeme practice listening exercises using movie clips and TV shows. The first few times, I could barely keep up with the plot. But slowly but surely, my ears started adjusting to processing continuous streams of dialogue.Next, I found some listening practice websites and podcasts for English learners. Boring at first, sure, but so helpful for training my brain to separate different voices and accents. Shoutout to that guy on "Culips Listening" who helped me finally distinguish between the "th" and "z" sounds!Finally, the breakthrough I'd been waiting for - I moved my desk right in front of the classroom speakers for our listening activities. No more spacing out or zoning out from the back row. With the audio clarity straight in my face, I was LOCKED IN.Did I still struggle occasionally and have to replay some tracks? You betcha. English listening is no joke when it's not your first language. But using those techniques, pre篇5Unit 4 Listening - My Struggles and TriumphsEnglish class was never my strong suit, especially the listening part. I just couldn't seem to keep up with thoserapid-fire recordings and make out all the words. When we started Unit 4 this semester, I braced myself for another round of frustration.The first listening exercise was about a family going on vacation. Simple enough, right? Wrong! The moment the track started playing, my mind went into panic mode. All I could hear was a jumbled mess of sounds. I strained my ears, desperately trying to latch onto any familiar words, but it was like they were speaking a foreign language at double speed.As the questions appeared on the screen, I felt my heart sink.I had absolutely no clue about the answers. Reluctantly, I filled ina few random guesses, knowing full well that I was bound to fail this section yet again.Dejected, I slumped in my seat, already dreading the next listening exercise. Little did I know, this unit would turn out to be a game-changer for me.Our teacher, Mrs. Smith, must have sensed the collective struggle in the classroom. She gathered us around and shared a simple yet brilliant tip: "Don't try to understand every single word. Focus on the main ideas and key details."It was like a lightbulb went off in my head. All this time, I had been so fixated on catching each and every word that I missed the bigger picture. With her guidance, we learned strategies to identify context clues, tone, and the overall gist of the conversations.The next few exercises were still challenging, but something had shifted within me. Instead of panicking, I took a deep breath and tried to tune into the main ideas. Slowly but surely, I started picking up on the critical information.One exercise was about two friends planning a camping trip. At first, the rapid back-and-forth banter threw me off. But then I caught on to key phrases like "next weekend," "hiking trail," and "bring a tent." Suddenly, the overall context became clear, and I could answer the questions with newfound confidence.With each successive exercise, my listening skills improved. I started recognizing common expressions and idioms, and I became better at separating relevant information from background noise.As we neared the end of Unit 4, I couldn't believe how far I had come. The final exercise was a news report about a local community event, and I managed to answer most of thequestions correctly. It was a small victory, but it felt monumental to me.Looking back, Unit 4 was a turning point in my English listening journey. It taught me the value of perseverance and the importance of not getting overwhelmed by the details. By focusing on the main ideas and key phrases, I was able to break through my listening comprehension barriers.Will I ace every listening exercise from now on? Probably not. But I've gained valuable strategies and a newfound confidence that will serve me well as I continue to navigate the challenges of English class.So, to anyone else struggling with listening comprehension, don't lose hope. Keep an open mind, focus on the big picture, and trust that with practice and the right mindset, those jumbled sounds will eventually start making sense.篇6My Exciting School TripHey friends! I'm so excited to tell you all about the awesome school trip I just went on last week. It was for our English listening class and man, did we have some fun adventures.It all started bright and early on Tuesday morning. Our teacher, Mrs. Johnson, had us all meet at the school bus loop at 7am sharp. I had stayed up way too late the night before playing video games, so I was pretty tired. But I could barely contain my excitement knowing we were going on a field trip!After we all piled onto the big yellow bus, Mrs. Johnson got on the microphone and gave us the rundown for the day. "Alright class, our first stop is going to be the radio station downtown to learn all about how they broadcast music, news, and other programs over the airwaves."The bus ride there seemed to take forever, but I didn't mind too much since I got to sit next to my best friend Jessica. We were gabbing away about all the latest gossip when finally, Mrs. Johnson announced we had arrived at 102.7 THE BEAT!We were greeted by one of the radio DJs named Josh. He was really friendly and had a great sense of humor as he gave us a tour of the studio. I couldn't believe all the crazy equipment they had to operate everything! Josh showed us the sound board, where they queue up songs, commercials, and sound effects. He even let a few of us put on headphones and read some lines over a fake radio broadcast which was super cool.After touring the radio station, we all piled back onto the bus and headed to our next destination - the local television news station, Channel 8 News. This place was even more high-tech than the radio station! We got to watch the news team in action filming their afternoon broadcast from the control room. It was fascinating seeing all the cameras, teleprompters, green screens, and other gear they use to make everything come together.During the commercial break, the meteorologist even brought a few of us on set to demonstrate how they use the green screen to show weather maps and graphics. I have to admit, I looked pretty goofy pretending to point at fake temperatures and storm systems! But it was still a really unique opportunity.After the news station, we stopped for a quick lunch at a pizza place downtown. Jessica and I split an extra large pepperoni pizza - it was so delicious but we probably shouldn't have eaten that much! We both had horrible stomachaches by the time we made it to our third and final destination of the day.Our last stop was the public library, which doesn't seem that exciting compared to the other places we visited. But it ended up being one of the highlights! We got to go behind the scenes and learn all about how libraries acquire, catalog, and shelve theirhuge book collections. We even got to sit in on a reading circle for little kids where the librarian did different voices and sound effects for all the different characters. It was honestly the cutest thing ever.At the end, the librarians had a surprise for us. They brought out these awesome 3D audio systems and let us experience what it's like to have stories come alive with immersive sound all around you. We listened to a few excerpts from popular audiobooks and it honestly felt like you were right there in the middle of the action. The effects were mind-blowing! I've never experienced books that way before but I am 100% going to start checking out more audiobooks from now on.As we headed back to the bus at the end of the day, I was exhausted but totally felt like I had learned so much about how sound and audio are used for radio, TV, libraries, and more. It really opened my eyes to all the careers out there that involve listening, broadcasting, and bringing stories to life through sound.Who knows, maybe I'll look into becoming a radio DJ, television personality, audiobook narrator, or even a sound engineer someday? One thing's for sure, this field trip gave me away deeper appreciation for audio and the roles it plays in our everyday lives. I can't wait for the next academic adventure!。
第五单元测评第一部分听力(共两节,满分30分)第一节(共5小题;每小题1.5分,满分7.5分)听下面5段对话。
每段对话后有一个小题,从题中所给的A、B、C三个选项中选出最佳选项。
听完每段对话后,你都有10秒钟的时间来回答有关小题和阅读下一小题。
每段对话仅读一遍。
1.What does the woman say about her job?A.It has proper working hours.B.It has too many women workers.C.It has too few female workers.2.What does the woman think of the apartment?A.It’s perfect.B.It’s e school.3.What will the girl do tomorrow?A.Watch TV.B.Take a test.C.Go to a movie.4.Where is the woman from?A.France.B.Germany.C.England.5.Where does the conversation take place?A.In a travel agency.B.In a hotel.C.In an art museum.第二节(共15小题;每小题1.5分,满分22.5分)听下面5段对话或独白。
每段对话或独白后有几个小题,从题中所给的A、B、C三个选项中选出最佳选项。
听每段对话或独白前,你将有时间阅读各个小题,每小题5秒钟;听完后,各小题将给出5秒钟的作答时间。
每段对话或独白读两遍。
听第6段材料,回答第6、7题。
6.Why is the man talking to the woman?A.She doesn’t have a ticket.B.She parks in the wrong place.C.She doesn’t pay the parking fe e.7.What does the woman suggest the man do?A.Remove the tree.B.Stay behind the tree.C.Keep the sign in plain sight.听第7段材料,回答第8至10题。
六年级英语作文教室规则不少于6句话全文共6篇示例,供读者参考篇1Classroom Rules Are Important!School is a really fun place to learn new things and make friends, but it can also be kind of confusing and overwhelming sometimes with all the different rules we have to follow. When I was in kindergarten, the only rules I really had to worry about were things like keeping my hands to myself, taking turns on the swings at recess, and cleaning up my art supplies. But now that I'm in 6th grade, there are so many more rules about how to behave properly in the classroom. At first, I thought having all these extra rules would just be a drag, but I've realized they actually make a lot of sense and help create an environment where everyone can learn effectively.One of the most important classroom rules is to raise your hand and wait to be called on before speaking. I'll admit, this one used to drive me crazy when I was younger because my mind would just start racing with all sorts of thoughts and questions that I wanted to blurt out immediately. My teachers always toldme that shouting out without raising my hand was disrupting the lesson and keeping other students from being able to concentrate. It took me awhile, but I've learned that following this rule shows respect for the teacher and my classmates. Now when I have something to say, I make sure to calmly raise my hand and wait my turn.Another key rule is to keep our desks and work areas neat and organized. I used to be a pretty messy and disorganized kid, with papers, pencils, and books just strewn all over my desk in a jumbled mess. My teachers would constantly tell me to straighten up because it was distracting and made it harder for me to focus on my work. At first I thought they were just being nitpicky, but then I started noticing how much easier it was to concentrate when my desktop was clear except for what I was currently working on. An organized space really does help create an organized mindset for learning.Not talking or causing disruptions while the teacher is giving instruction is another biggie. I'll sheepishly admit that I got in trouble quite a few times when I was younger for whispering to my friends or making silly noises when I got restless during lessons. My parents had to come in for conferences because I just couldn't seem to settle down and pay attention. Eventually itclicked that I wasn't just annoying the teacher - I was shortchanging myself by not listening properly and hindering my own ability to learn the material being taught. These days I really make an effort to give the teacher my full attention and stay quiet unless I'm asked to respond.There are also rules about things like not running or roughhousing in the classroom, not chewing gum or eating snacks unless permitted, and treating books, materials and school property with care. While these seem like common sense, you'd be surprised how many times I've seen kids get carried away and accidentally knock things over or spill food and drinks at their desks. I get it - sometimes you just get the urge to jump around or you get really hungry during a long stretch of sitting still. But the rules are in place to prevent any safety issues or damage, and to make sure we have an environment suitable for learning.Speaking of which, there are also rules about using technology appropriately in the classroom. These days, we're allowed to use laptops, tablets and phones for certain lessons and projects. But there are strict policies about not accessing non-educational websites or apps, not taking photos or recording videos without permission, and not texting or playinggames when we're supposed to be working. I have to admit I've been guilty of getting busted looking at memes or watching YouTube videos when I should have been focused on an assignment. But I've learned that giving in to those temptations ends up being a waste of time that sets me behind.At the end of the day, I realize that all these classroom rules aren't just about making students behave or making the teacher's life easier. They create an environment that facilitates learning and allows every student to get the most out of their education. And really, that's what we're all here for - to expand our knowledge and get prepared for the challenges of middle school, high school and beyond. Following the basic rules of being attentive, organized and respectful in the classroom puts us in the best possible position to reach our full potential. So even though some of the rules may seem annoying at times, I've come to understand why they're important and how they truly benefit all of us students. I'm glad my school has these guidelines in place to help us all succeed!篇2Classroom Rules Are No Fun!Rules, rules, rules! That's all we ever hear about in school. Follow the rules, obey the rules, don't break the rules. It's enough to drive a kid crazy! But I get it, I really do. Rules are important for keeping order and making sure everyone is safe and able to learn. Still, that doesn't make them any less annoying sometimes.Take the classroom rules for example. Our teacher, Mrs. Johnson, has about a million of them posted up on the walls. Raise your hand before speaking, keep your hands and feet to yourself, no eating or drinking except for water, treat others with kindness and respect. On and on it goes. Don't get me wrong, I understand why we need those rules. Can you imagine if we could just blurt things out whenever we wanted? The classroom would be pure chaos! Or if kids were allowed to run around hitting and kicking each other? That would not be a very nice place to try and learn.But some of the rules seem a little over the top if you ask me. Like the no eating or drinking rule. I get that we shouldn't be chowing down on a full meal during class. That would be messy and distracting. But something small and neat like a granola bar or juice box? What's the harm in that, especially if you're really hungry? Standing up all period without a little snack can make it hard to concentrate. And forget about chewing gum - that's anabsolute no-no according to the rules. I don't see why we can't be trusted to be responsible with gum as long as we don't make noise with it or stick it under the desks.Then there are the rules about bathroom breaks. We're only allowed to go during the breaks between classes unless it's a true emergency. But what if you really have to go during class? Sitting there holding it in for over an hour can be so uncomfortable! I understand we can't have kids getting up to use the bathroom every five minutes, but they could probably make an exception if you raise your hand politely and ask. Instead, we get the dreaded "You should have gone during passing period" response. Seriously?One of my biggest gripes is the no phones or electronics rule.I know we're supposed to be focused on our lessons, but a little music on headphones can actually help some kids concentrate better. As long as you're still paying attention and not disturbing others, what's the problem? And phones can be used for lots of educational things too, like looking up information or working on writing assignments. Of course, there's always the risk of getting distracted by social media or games. But we're in 6th grade, almost teenagers! We should be trusted with a little responsibility by now.That's another thing about all these rules - they treat us like we're so much younger than we are. We're the oldest kids in elementary school now. Next year, we'll be in middle school and have a lot more independence. But the way some of these rules are enforced, you'd think we were still in 2nd grade. Take the silent lunch rule for instance. We have to spend 20 minutes of our 30 minute lunch period in total silence so we can "rest our brains." What is this, kindergarten nap time? We're 11 and 12 years old! We can handle eating while having conversations like normal human beings. Being forced to stay silent that long just leads to tons of kids getting in trouble for whispering to their friends.I could go on and on about the classroom rules I think are too strict or don't make sense. But I also get that Mrs. Johnson and the rest of the teachers are just trying to create an environment where everyone can learn. Having no rules would lead to anarchy. And some of them, like being respectful and keeping our hands to ourselves, are just common decency. Even us kids know that much.I guess what I'm saying is that a few of the rules could be updated or relaxed a little to treat us more like the young adults we're becoming. We're not perfect, but we're also not little kidsanymore. If they loosen the reins bit by bit this year, it'll help us get ready for more independence and responsibility in middle school. Just some food for thought!At the end of the day, we know the classroom rules are there for a reason. We might grumble and complain about them, but most of us don't want total rule-free chaos either. A little balance and understanding from both sides could go a long way though. We'll follow the rules better if we feel respected and trusted in return. Having that open conversation is probably better than laying down a long list of rules to be strictly obeyed without question. Because let's be honest, us kids are going to question them no matter what!篇3Classroom Rules: The Keys to a Successful Learning EnvironmentAs a 6th grader, I've come to realize that having a set of clear and fair classroom rules is crucial for creating a positive and productive learning environment. These rules not only help maintain order and discipline but also foster a sense of respect and camaraderie among students and teachers alike.One of the most important rules in our classroom is raising your hand before speaking. This simple act of courtesy ensures that everyone has an opportunity to express their thoughts without interrupting others. It teaches us the value of active listening and encourages us to be patient and considerate of our classmates.Another essential rule is to come to class prepared. This means bringing all the necessary materials, such as textbooks, notebooks, pens, and pencils. Being prepared not only demonstrates our commitment to learning but also helps maximize our time in class. It's frustrating when someone constantly forgets their supplies, as it disrupts the flow of the lesson and wastes valuable instructional time.Respecting others is a fundamental rule that extends beyond just our classmates. It includes showing respect to our teachers, school staff, and the school property itself. We are expected to treat everyone with kindness and consideration, regardless of their background or differences. This rule promotes a sense of inclusivity and fosters a safe and welcoming environment for all.Maintaining a clean and organized classroom is another important rule. We take turns being responsible for tasks like wiping the whiteboard, collecting homework, or straightening upthe desks at the end of the day. These small acts of responsibility not only keep our learning space tidy but also instill a sense of ownership and pride in our classroom.Lastly, a rule that may seem trivial but holds significant importance is punctuality. Arriving to class on time shows respect for our teachers and classmates, and it ensures that we do not miss out on valuable instructional time. Being punctual also helps us develop essential time management skills that will serve us well in our future academic and professional endeavors.While these rules may seem straightforward, adhering to them consistently can be challenging at times. We are still young and learning, and mistakes are bound to happen. However, by consistently reinforcing these rules and leading by example, our teachers create an environment that fosters discipline, respect, and a love for learning.In conclusion, classroom rules are not mere guidelines but rather the foundation upon which a successful and enriching learning experience is built. They teach us valuable life lessons, such as respect, responsibility, and integrity, while ensuring that our classroom remains a safe and productive space for everyone. As students, it is our responsibility to embrace and uphold theserules, for they are the keys that unlock the doors to our academic and personal growth.篇4Classroom Rules: The Do's and Don'ts of Being a Good StudentBeing a good student is not just about getting good grades. It's also about following the classroom rules and being respectful to your teacher and classmates. In my 6th grade class, we have a bunch of rules that we all need to follow. Some of them are pretty obvious, but others might surprise you!First and foremost, we have to raise our hand if we want to speak. No blurting out answers or comments without permission! That's just rude and disrupts the whole class. Mrs. Johnson always says, "Raise your hand and wait to be called on. That's how we maintain order in our classroom community." I'll admit, sometimes it's really hard to keep my hand down when I know the answer. But I've learned that patience is a virtue, and my turn will come if I just wait my turn.Another big rule is no eating or drinking in class, except for water bottles. I get why this rule exists – can you imagine if we all brought snacks and sodas and juice boxes to class every day? Itwould be a huge mess, with crumbs and spills everywhere. Plus, crunchy snacks are so distracting when you're trying to concentrate. Still, I have to say, I get pretty hungry sometimes during our double math period in the afternoon. A small snack would really hit the spot! But rules are rules.Speaking of math, we're not allowed to use our phones or smart watches during tests and quizzes. That's kind of ano-brainer – how else could the teacher know we're not cheating or looking up answers online? Personally, I don't even really want my phone out during class time. It's too tempting to get distracted by games or social media. I try to put it away and focus.One rule that seemed a little silly at first was the ban on hats and hoods in the classroom. But Mrs. Johnson explained that it's easier for her to see our faces and make eye contact when our heads aren't covered up. Plus, it's kind of rude and makes you look disengaged when you're wearing a baseball cap pulled down low. I get it now – we take off our hats to show respect and pay attention.Along those same lines, we're expected to clean up after ourselves and not make a huge mess. Throwing away our trash, pushing in our chairs, keeping our desks neat and organized –it's all part of being a respectful classmate. Our classroom belongs to all of us, so we all need to do our part to take care of it. Nobody wants their pencil rolling away into a giant pit of crumbs and old papers, right?There are probably a bunch of other little rules I'm forgetting, but those are some of the big ones that come to mind. Do I follow every single rule perfectly, every single day? Of course not – I'm only human (and a 6th grader at that!). Sometimes I get chatty and need a reminder to raise my hand. Sometimes my water bottle leaks a little on my desk and I have to wipe it up. We all slip up now and then.But for the most part, I really do try my best to be arule-following student. The rules are there for a reason – to help our classroom run smoothly and be a positive learning environment for everyone. When we all follow the rules and respect each other, it allows Mrs. Johnson to teach us properly. We can focus, participate, and actually learn something!So while some of the rules might seem annoying or pointless at times, they're all in place to help us be better students. If we can embrace the classroom rules instead of fighting against them, I think we'll all have a much better year. Let's work together, 6th graders! Our education is important.篇5Classroom Rules: The Key to a Productive Learning EnvironmentAs a 6th grader, I've come to realize that having rules in the classroom is crucial for creating a conducive learning environment. These rules aren't meant to be restrictive or oppressive; instead, they're designed to foster an atmosphere of respect, order, and productivity.One of the most important rules in our classroom is raising your hand before speaking. This simple act ensures that everyone has a chance to express their thoughts without interrupting others. It teaches us patience and the value of active listening, which are essential skills for effective communication.Another rule that I've grown to appreciate is being on time for class. Punctuality not only shows respect for the teacher and fellow classmates but also helps minimize disruptions and ensures that we don't miss out on valuable learning opportunities. It's a habit that will serve us well in our future academic and professional endeavors.Maintaining a clean and organized classroom is another rule that we must follow. A tidy workspace can significantly impactour ability to concentrate and stay focused. By keeping our desks and surrounding areas neat, we create a sense of order that can positively influence our mindset and productivity.One rule that might seem trivial but holds great significance is dressing appropriately for school. While we may not fully comprehend the reasoning behind it now, adhering to a dress code instills a sense of professionalism and respect for the educational institution. It also helps us prepare for future environments where dress codes are common, such as workplaces or formal events.Treating others with kindness and respect is a rule that extends beyond the classroom walls. Bullying, name-calling, and any form of disrespectful behavior towards our classmates or teachers is strictly prohibited. This rule fosters an inclusive and welcoming environment where everyone feels safe, valued, and encouraged to learn and grow.While some rules may seem inconvenient or unnecessary at first, as we grow older, we begin to recognize their value. They teach us discipline, respect, and the importance of creating a harmonious and productive environment for everyone involved in the learning process.In conclusion, classroom rules play a vital role in shaping our educational journey. They instill valuable life skills, foster a positive learning environment, and prepare us for the challenges and expectations we'll face in the future. By embracing these rules and understanding their significance, we not only enhance our academic experience but also develop qualities that will serve us well in all aspects of life.篇6Classroom Rules Are No Fun!School rules can be such a drag sometimes! Especially those pesky classroom rules we have to follow every single day. I get that they are important for keeping order and letting us learn, but man do they put a damper on my fun.First off, we have to raise our hand to speak in class. I understand the reason for this rule - to prevent total chaos with 30 kids all yapping at once. But it's really hard for me to sit patiently with my hand up, just waiting to be called on, when I'm bubbling over with something to say. Sometimes I get so impatient that I blurt it out and get in trouble. Oops!Then there's the rule about staying seated unless you have permission to get up. Did you know that wiggling around in yourseat or leaning back on your chair can get you a warning? I'm an energetic kid and it's torture to have to sit perfectly still for hours on end. I play sports after school to burn off energy, but mornings are tough when I'm cooped up in class.Speaking of mornings, we're not allowed to chew gum or eat any food unless it's snack time. I get hungry long before lunch and my stomach is grumbling away. Once I snuck a granola bar into class and tried to secretly eat it under my desk. That was a mistake - Mrs. Johnson has eyes in the back of her head! She caught me red-handed and gave me a detention.Possibly the most annoying rule of all is no talking when the teacher is talking. I really struggle with this one. I get so excited and have so much I want to share that I frequently interrupt Mrs. Johnson in the middle of her lessons. I'll be calling out with my hand raised, unable to contain myself, and she has to stop and remind me of the rule. It's bad, I know, but I honestly can't help myself sometimes.There are a bunch of other rules too - no running in the halls, no hitting/pushing, no inappropriate language, and always being prepared with pencils and homework. They all make sense from the teacher's perspective. But from a 6th grader's point of view, they put a real damper on our fun and self-expression.I get that we need rules to keep a big group under control. And I know they're enforced to help create an environment where we can learn and thrive. But following all those rules day after day can make a kid go stir-crazy! We're just bouncy little bundles of energy trying to enjoy our childhood. School would be so much more fun if the rules could be a bit more relaxed.When I'm king of the world, there will be no classroom rules. We can all just do whatever we want! Talk with our mouths full of gum, kick back in our chairs, move around the classroom freely, and shout out thoughts without raising our hands. Absolute chaos? Yes. But at least it would be exciting!For now, I'll try my best to follow Mrs. Johnson's rules, even if they are a total buzzkill at times. I understand that the rules are in place to create structure and allow us all to learn more effectively. And I'd certainly never want to be kicked out of class for not following them. So I'll do my best to be a model student and leader for my classmates.But that doesn't mean I have to like the rules. In fact, I'd go so far as to say most rules are stupid and should be ignored! Okay, maybe I shouldn't go quite that far since rules do serve a purpose. Let's just say that my dream school would have very few rules beyond the basics of being kind and letting everyonehave their turn. No more raising hands, staying seated, or eating restrictions. We'd learn better that way anyhow since we could move freely and fuel our bodies as needed.Well, there's my impassioned plea against restrictive classroom rules. I hope none of my teachers read this essay! Just kidding...kind of. At the end of the day, I know the rules are in place for good reason. But they still cramp my wild child style more often than not. Maybe one day I'll be a teacher and get to make the rules myself. Then you'd better believe there would be a lot more fun and self-expression allowed!。
六年级下册第一单元考试必考作文英语全文共6篇示例,供读者参考篇1English Exam Topics for Unit 1 of 6th GradeHi everyone! I'm so excited to share with you what I've learned about the English exam topics for Unit 1 of our 6th grade textbook. Get ready, because there's a lot to cover!First up, we need to know all about verb tenses. Our teacher says verb tenses are like a secret code that lets us talk about when stuff happened. The simple present tense is for things happening now, like "I study English." Pretty easy, right? Then there's the present continuous tense for things happening right now, like "I am studying English." Using the right tense is super important if you want people to understand you.Next, we have to master subject-verb agreement. That just means making sure your subjects and verbs agree with each other. So you say "She walks" not "She walk." It's kind of like giving your subjects and verbs a secret handshake. If they don't match up, it's a grammar crime!Okay, now for one of my favorite topics - adjectives! Adjectives are awesome describing words that make your writing way more interesting. Like instead of saying "I have a dog," you can say "I have a furry, cute dog." See how much better that is? The more descriptive adjectives you use, the more it paints a picture in someone's mind. Just don't go too overboard and use a million adjectives for every sentence.Speaking of making writing interesting, we also need to know about adverbs. Adverbs describe verbs and adjectives, and they usually end in -ly, like "slowly" or "happily." Using good adverbs really brings your writing to life. For example, "The tortoise crawled slowly across the road" is way better than just saying "The tortoise crawled."This unit also covers punctuation, which is code for all those crazy symbols like periods, commas, apostrophes, and more. It might seem boring, but punctuation is secret spy language that tells you how to read a sentence properly. You have to get it right, or your writing will be all messed up.Another key topic is sentence structure - making sure you build complete, logical sentences. Every sentence needs a subject (what it's about) and a predicate (what's being said about the subject). It's like giving your sentences a strong foundationso they don't collapse into bad grammar. Sentence structure might take some practice, but it's important.We're also learning about capitalization rules. You have to capitalize proper nouns (names of specific people, places, or things) and the first word of every sentence. It's an easy way to show you respect those words and sentences. Just don't go capitalizing every single word unless you're writing titles or something.Finally, one of the trickiest parts is understanding prepositions - tiny words like "in," "on," and "under" that show location or direction. You wouldn't believe how much trouble those little prepositions can cause if you use them incorrectly! We have to memorize common preposition rules so our sentences make sense.Whew, I think that covers all the major grammar concepts for the first unit exam! As you can see, there's a ton we need to know about building proper English sentences. The good news is that the more we practice, the easier it will get. Our teacher says grammar is like a secret language, and once we learn the code, we'll be masters of communication!So let's study hard, keep practicing, and get ready to rock that Unit 1 exam. Grammar skills are super important not just fortests, but for clearly expressing our thoughts and ideas. With the right tools, we can all become awesome writers and speakers. Who's excited to unlock the secrets of English grammar? I know I am! Let's do this!篇2Unit 1 English Exam Writing TopicsHey there! It's me again, ready to share all the awesome English writing topics we might see on the Unit 1 exam. Get those pencils ready, 'cause this is gonna be a long one!Our first potential topic is "My Summer Vacation". Oh man, I've got so much to say about that! You could write about any fun trips you took, or adventures you had right in your own neighborhood. Maybe you went camping, or to the beach, or just had a bunch of sleepovers and played video games all summer. Whatever you did, just make sure to pack your essay full of cool details and descriptions so your teacher can picture it perfectly.Another topic could be "My Favorite Book". Doesn't matter if it's a comic book, a sci-fi novel, or a classic work of literature - just tell your teacher why you love it so much! Describe the characters, the plot, the setting, and most importantly, what it means to you and why you'd recommend it to a friend.Or how about "An Influential Person in My Life"? You could write about a parent, grandparent, teacher, coach, or anyone else who has really shaped who you are. Share some stories about them and explain what makes them such a great role model. Get those creative writing muscles flexed!Here's a fun one: "If I Could Have Any Superpower". Honestly, what 6th grader doesn't dream about having superpowers? Invisibility, flying, super strength - the possibilities are endless! Describe which power you'd want and why, then let your imagination run wild with all the awesome things you'd be able to do. Just try not to use your powers for anything too crazy!You might also get a prompt like "My Favorite Holiday". Whether it's your birthday, Christmas, Thanksgiving, or a cultural holiday you celebrate, reminisce about the very best holiday memories and traditions from your life so far. Don't forget to add in all those juicy descriptive details about the food, decorations, activities, and quality family time.One more idea could be "My Goals for the Future". What do you want to be when you grow up? Where do you want to live? What kinds of adventures do you dream about having? Let your teacher know all about your biggest hopes and dreams for the next chapter of your life after finishing elementary school.Those are just a few of the potential writing prompts that might show up on the Unit 1 exam. No matter which one you get, just take a deep breath, collect your thoughts, and start writing from the heart. Add some fun descriptions, tell a few anecdotes, explain your opinions, and above all, let your creativity shine! You've got this!Well, that about covers all the juicy exam info I can dish out for now. Hopefully this helps you feel prepared and pumped to knock that writing section right out of the park. Just remember to read the questions carefully, plan out your thoughts, write neatly, and believe in yourself. You're gonna do great!Now if you'll excuse me, I've got some video games calling my name. Gotta prepare for that hypothetical summer vacation essay, am I right? Happy studying, and let me know if you need any more tips!篇3My Awesome Summer VacationHey there! My name is Jamie and I'm a 6th grader. I just had the most amazing summer break ever and I can't wait to tell you all about it! We went on this really cool family vacation for twowhole weeks. It was so much fun and I have a ton of great stories to share with you.It all started when my dad surprised us one night at dinner by announcing we were going on a big road trip! My little brother Timmy and I were super excited. Mom seemed a little nervous about the whole thing, but Dad promised he had planned everything out perfectly.The next morning, we all piled into our mini-van and hit the road bright and early. Our first stop was the Grand Canyon in Arizona. Have you ever seen pictures of it? It's this massive canyon that just seems to go on forever. But seeing it in person was even more incredible! We took a little hike along the rim trail and the views were just breathtaking. Timmy kept saying he felt like a tiny ant next to something so enormous.After that, we drove over to California to check out some beaches. I had never really seen the Pacific Ocean before, except in movies. It's way bigger than I imagined! We spent a couple days just hanging out on the beach, building sandcastles, and swimming in the waves. Timmy even saw a few dolphins jumping out of the water which was so cool.From California, we headed up the coast into Oregon. This was probably my favorite part because we went on an awesomewhitewater rafting trip! We all had to wear these big bulky lifejackets and sit in this huge rubber raft. Then our guide started rowing us right into the middle of this rushing river full of choppy waves and rapids. It was simultaneously terrifying and thrilling! At one point a huge wave crashed over us and I thought we were goners for sure. But our guide stayed totally calm and got us through it safely. What an adrenaline rush!The next few days, we just kind of meandered around Oregon, stopping at different little towns and tourist traps along the way. We saw some really pretty waterfalls, went to some cheesy roadside attractions, and I even got to go gemstone mining which was awesome. I found a few semi-precious stones that I'm going to get made into a necklace.Finally, we made our way up to Washington to visit Seattle for a few days before heading home. We went up in the Space Needle, which is this massive tower with an observation deck and a revolving restaurant at the top. The views from up there were unbelievable! You could see the entire city skyline, the mountains, the ocean, everything. We also went to the Museum of Pop Culture which was right up my alley. They had all these awesome exhibits about music, movies, video games and more. I'm such a pop culture junkie so I was in heaven!On our last day, we stopped at Mount Rainier National Park to do some hiking and take in the scenery. That place is just gorgeous with its meadows full of wildflowers, the bright blue lakes, and the big snowy mountain looming in the background. We even saw a black bear lumbering around from a distance which was really neat. Although I have to admit, I was a little nervous it might try to get into our food!After two weeks of non-stop adventuring and making so many incredible memories, it was finally time to head back home.I was篇4My Awesome Summer VacationWow, can you believe we're already in sixth grade? It feels like just yesterday we were those tiny kindergarteners learning to tie our shoes and spell C-A-T. Now we're the big kids on campus!I can't wait to tell you all about my amazing summer vacation.It started off with a bang the day after school let out. My parents surprised me and my little brother Timmy by telling us we were going to Disneyland! I'd been begging to go for years but they always said it was too expensive. Apparently my dad gota bonus at work this year though, so abracadabra, we were California-bound!The drive took forever. Timmy and I were basically asking "Are we there yet?" every five minutes. But when we finally saw those iconic Mickey Mouse ears peeking over the entrance gates, we went wild! I don't think I've ever felt so excited.We stayed at one of the Disney resorts right on the park grounds. Our room had a massive TV, a cozy king-sized bed, and best of all, we could see the fireworks show every night just by opening our curtains! My mom said it was pricey but totally worth it to stay in the "Disney Bubble."Our first day at the parks, we hit all the classic rides - Space Mountain, Splash Mountain, Big Thunder Railroad. I'm a total thrill junkie so I loved all the huge coasters and drops. Timmy's not quite as brave though. He refused to go on anything big and scary!My favorite was probably Indiana Jones Adventure. You get to go on this epic expedition and there are tons of cool effects like shooting fire and massive boulder traps you barely escape from. I felt like a real-life action hero!We spent six days total exploring Disneyland and California Adventure. I'm pretty sure we went on every single ride and watched every parade and show. We even got to meet Mickey, Minnie, Goofy, and the whole crew for hugs and photos!Needless to say, by the end of the week my parents were completely Disney'd out. But not me and Timmy - we could have stayed forever! We begged and pleaded to hit up the California beaches before driving home. After some intense negotiating, mom and dad agreed to a two-day detour.First up was Santa Monica pier, with its iconic ferris wheel and crazy street performers everywhere you looked. We rode the roller coaster that goes right over the ocean, drooled over some delicious-smelling soft pretzels, and picked up some treasured souvenir shot glasses for my nana's collection.Then it was off to Malibu's beaches for a full day of swimming, sandcastle-making, and getting our tan on. The water was perfect - not too cold like up here in the Northwest. And the waves were just the right size for awesome boogie boarding. We must have gone down those waves a million times!On the drive home, Timmy and I chatted nonstop about our favorite parts. We both agreed that the Indiana Jones ride was the most thrilling, and that Disneyland's fireworks show washands-down the most magical. I also LOVED seeing how excited the little kids got to meet their heroes like Buzz Lightyear and Cinderella.Of course, I missed my friends like crazy during those two weeks. And sleeping in my own bed again felt like heaven after all that traveling. But man, I would do that trip over and over again in a heartbeat! I'm so grateful my parents made it happen for us.Anyways, that's my big summer adventure in a nutshell. Beats sitting around playing video games all break, am I right? I can't wait to get back to the classroom and finally get to practice speaking in complete sentences again. Sixth grade, here we come!篇5My Awesome Summer Vacation!Wow, where do I even begin? This was the most fun, exciting, and adventurous summer vacation ever! I had such an amazing time. Let me tell you all about it!It started when school let out in late June. Finally, no more teachers, no more homework, no more waking up super early.Just months of freedom and fun ahead! My parents had some really cool stuff planned for us too.First up was a road trip to visit my cousins in another state. I was a little nervous because the drive was going to take like 8 hours. That's so long to be stuck in the car! But my mom packed tons of road trip games, snacks, and movies for the portable DVD player. It ended up being a blast.When we got to my cousins' house, it was just a big kid party all week long. We went swimming every day in their awesome pool. We had big family barbecues and roasted marshmallows. We stayed up late every night watching movies, playing video games, and having awesome sleepovers. I didn't want to leave!But the fun was just getting started. After visiting the cousins, we headed to the beach for a whole week! This was going to be epic. As soon as we got to the beach house, I raced upstairs to claim the sickest room. It had its own balcony overlooking the ocean. I could smell the salty sea air. So cool!We spent every single day at the beach that week. We swam, boogie boarded, built crazy sandcastles, and played frisbee and beach games. The sand was so hot it burned your feet! One day we even saw some dolphins swimming really close to the shore.At night, we'd go get ice cream and walk along the boardwalk. There were so many bright lights and people everywhere.Sadly, beach week had to end eventually. But the good news was that my best friend's birthday was coming up! Her parents planned this massive blowout birthday bash at a trampoline park. We jumped on all the trampolines for hours, played trampoline dodgeball, and practiced our sickest flips and tricks. Afterwards, we had a huge pizza party in the private party room. We played games, ate cake, and my friend got a mountain of awesome presents. It was an epic birthday celebration.Just when I thought the fun was over, my parents surprised me and my brother with tickets to go to SUMMER CAMP for two whole weeks! Neither of us had ever been to overnight camp before. We were a little nervous at first, but it ended up being the highlight of the entire summer.Camp was everything I dreamed it would be and more. We stayed in cool log cabins right near the lake. We made so many new friends from all around the country. We went canoeing, hiking, swimming in the lake, made tie-dye shirts, sang crazy songs by the campfire, and did talent shows. The counselors were just crazy fun too.One of the coolest parts was actually getting to try out being a C.I.T (counselor in training). For an entire day, me and a few other kids got to help out and be "counselors." We felt so mature and grown up! Although to be honest, we probably weren't very good at it and just added to the chaos.I'll never forget that last big campfire on the final night of camp. We sang goofy camp songs, told hilarious and spooky stories, and stuffed our faces with s'mores. When it was over, we all headed back to our cabins and had one final sleepover altogether. We stayed up whispering, laughing, and reminiscing about the incredible two weeks we just had. We made so many amazing memories that would last forever.The next morning was definitely sad having to say goodbye to all our new camp friends and pack up to go home. But after the summer I just had, I felt like the luckiest kid ever. I got to go on an awesome road trip, stay at the beach for a week, go to an epic birthday party, and experience the time of my life at sleep away camp. It was just the absolute best summer ever.I can't wait to see what sort of adventures next summer will bring! Although it will be pretty hard to top this one. Maybe we'll go on a cruise, take a trip to Disney World, or find somethingelse that's just as fun and exciting. One thing's for sure, this was a summer I'll never forget!篇6My Awesome English ClassHi there! My name is Emma and I'm a sixth grader. I really love my English class this year - it's so much fun! Mrs. Thompson is the best teacher ever. She always makes learning English super interesting and never boring.In the first unit this year, we learned all about different cultures and countries around the world. It was so cool getting to learn about how kids my age live in other places. We read stories, watched videos, and even had some special guests come to our class to teach us!One of my favorite things we did was learn about holidays and celebrations in other countries. Did you know that in Sweden, they celebrate St. Lucia Day on December 13th? A girl gets dressed up like a Swedish Christmas bride with a wreath of candles on her head. So pretty! We made our own St. Lucia crowns out of construction paper and battery-operated tea lights. I wore mine around the house and my little brother thought I was the Swedish Christmas Queen.We also learned all about Holi, the Indian festival of colors. It looked like so much fun - everyone throws colorful powder all over each other! We got to come to class in our grubbiest clothes and have a little Holi celebration of our own. I ended up looking like a rainbow by the end of it. My mom wasn't too happy about the mess, but I had a blast!Another awesome thing we did was learn about foods from around the world. Mrs. Thompson had us all bring in a cultural dish from our own families. I'm half Mexican, so my mom made her famous tamales. My best friend Jayden's family is from Thailand, so he brought pad thai. Everything smelled amazing and tasted so delicious! We all got to try a little bit of every dish. It was like having a fancy international dinner right in our classroom.Of course, we also learned lots of new English vocabulary words and grammar rules. But Mrs. Thompson always made it fun with games and activities. Like this one time, we played English vocabulary bingo. Another time, we went on a scavenger hunt around the school looking for objects that matched the new words we learned. Anything to get us up and moving around instead of just sitting at our desks!For our final unit project, we had to pick a country and make a big presentation board all about it. I chose Mexico since that's part of my heritage. I included facts about the geography, famous landmarks, traditional clothing, holidays, food, and more.I even did a section on important people from Mexico like artists Frida Kahlo and Diego Rivera.Then we had a International Culture Fair at school one evening. All the sixth graders set up our project boards in the cafeteria like it was a museum exhibit. We got to walk around, check out everyone else's boards, sample yummy ethnic snacks, and even do some fun activities like making Chinese lanterns. My parents were so impressed by all the hard work we did!I definitely learned SO much this unit. Not just about other cultures, but how to be a good student - pay attention, take good notes, study hard, and put a lot of effort into big projects. Mrs. Thompson pushes us to do our best because she knows we can.English class has always been one of my favorite subjects, but this unit made me love it even more. Learning about the world around us is so fascinating. There's so much amazing diversity out there when it comes to how people look, what they eat, what holidays they celebrate, and more. But there's also somuch that connects us all together too, no matter where we're from.I'm already excited to see what we're going to learn in the next unit. Maybe we'll do creative writing and I'll get to write my own short stories and poems. Or perhaps we'll study drama and act out some famous plays. As long as it's with Mrs. Thompson's fun and energetic teaching style, I know it's going to be awesome. Bring it on!。
M.-S. Chen, P.S. Yu, and B. Liu (Eds.): PAKDD 2002, LNAI 2336, pp. 334-340, 2002.© Springer-Verlag Berlin Heidelberg 2002Top Down FP-Growth for Association Rule MiningKe Wang, Liu Tang, Jiawei Han, and Junqiang LiuSchool of Computing Science, Simon Fraser University {wangk, llt, han, jliui}@cs.sfu.caAbstract. In this paper, we propose an efficient algorithm, called TD-FP-Growth (the shorthand for Top-Down FP-Growth), to mine frequent patterns.TD-FP-Growth searches the FP-tree in the top-down order, as opposed to the bottom-up order of previously proposed FP-Growth. The advantage of the top-down search is not generating conditional pattern bases and sub-FP-trees, thus,saving substantial amount of time and space. We extend TD-FP-Growth to mine association rules by applying two new pruning strategies: one is to push multiple minimum supports and the other is to push the minimum confidence.Experiments show that these algorithms and strategies are highly effective in reducing the search space.1 IntroductionAssociation rule mining has many important applications in real life. An association rule represents an interesting relationship written as B A ⇒, read as “if A occurs,then B likely occurs”. The probability that both A and B occur is called the support ,and written as count (AB). The probability that B occurs given that A has occurred is called the confidence . The association rule mining problem is to find all association rules above the user-specified minimum support and minimum confidence. This is done in two steps: step 1, find all frequent patterns ; step 2, generate association rules from frequent patterns .This two-step mining approach suffers from several drawbacks. First, only a single uniform minimum support is used, though the distribution of data in reality is not uniform. Second, the two-step process does not consider the confidence constraint at all during the first step. Pushing the confidence constraint into the first step can further reduce search space and hence improve efficiency.In this paper, we develop a family of algorithms, called TD-FP-Growth , for mining frequent patterns and association rules. Instead of exploring the FP-tree in the bottom-up order as in [5], TD-FP-Growth explores the FP-tree in the top-down order. The advantage of the top-down search is not constructing conditional pattern bases and sub-trees as in [5]. We then extend TD-FP-Growth to mine association rules by applying two new pruning strategies: TD-FP-Growth(M) pushes multiple minimum supports and TD-FP-Growth(C) pushes the minimum confidence.2 Related WorkSince its introduction [1], the problem of mining association rules has been the subject of many studies [8][9][10][11]. The most well known method is the Apriori’s anti-monotone strategy for finding frequent patterns [3]. However, this method suffersTop Down FP-Growth for Association Rule Mining 335 from generating too many candidates. To avoid generating many candidates, [4] proposes to represent the database by a frequent pattern tree (called the FP-tree). The FP-tree is searched recursively in a bottom-up order to grow longer patterns from shorter ones. This algorithm needs to build conditional pattern bases and sub-FP-trees for each shorter pattern in order to search for longer patterns, thus, becomes very time and space consuming as the recursion goes deep and the number of patterns goes large.As far as we know, [7][8] are the only works to explicitly deal with non-uniform minimum support. In [7], a minimum item support (MIS) is associated with each item. [8] bins items according to support and specifies the minimum support for combinations of bins. Unlike those works, our specification of minimum support is associated with the consequent of a rule, not with an arbitrary item or pattern.3 TD-FP-Growth for Frequent Pattern MiningAs in [5], TD-FP-Growth first constructs the FP-tree in two scans of the database. In the first scan, we accumulate the count for each item. In the second scan, only the frequent items in each transaction are inserted as a node into the FP-tree. Two transactions share the same upper path if their first few frequent items are same. Each node in the tree is labeled by an item. An I_node refers to a node labeled by item I. For each item I, all I_nodes are linked by a side-link. Associated with each node v is a count, denoted by count(v), representing the number of transactions that pass through the node. At the same time, a header table H(Item, count, side-link) is built. An entry (I, H(I),ptr) in the header table records the total count and the head of the side-link for item I, denoted by H(I) and ptr respectively. Importantly, the items in each transaction are lexicographically ordered, and so are the labels on each path in FP-tree and the entries in a header table. We use Example 3.1 to illustrate the idea of TD-FP-Growth.Example 3.1 A transaction database is given as in the following Figure 3.1. Suppose that the minimum support is 2. After two scans of transaction database, the FP-tree and the header table H is built as Figure 3.1.The top-down mining of FP-tree is described below. First, entry a at the top of H is frequent. Since a_node only appears on the first level of the FP-tree, we just need to output {aFigure 3.1 Transaction table, FP-tree and HThen, for entry b in H, following the side-link of b, we walk up the paths starting from b_node in the FP-tree once to build a sub-header-table for b, denoted H_b.336 Ke Wang et al.These paths are in bold face in Figure 3.2. During the walk up, we link up encountered nodes of the same label by a side-link, and accumulate the count for such nodes. In Figure 3.2, there are two paths starting with b_node: root-b and root-a-b. By walking up these paths, we accumulate the count of the a_node to 1 because path root-a-b only occurs once in the database. We also create an entry a in the sub-header table H_b. The count of entry a is 1 since the count of a is actually the count of pattern {a, b}.a_node is now linked by the side-link for entry a in H_b and the count is modified to 1. Since the minimum support is 2, pattern {a, b} is infrequent. This finishes mining patterns with b as the last item. H_b now can be deleted from the memory. If pattern {a, b} is frequent, we will continue to build sub-header-table H_ab and mine FP-tree recursively. In general, when we consider an entry I in H_x, we will mine all frequent patterns that end up with Ix. In this way, TD-FP-GrowthSimilarly, we find frequent patterns: {c}, {b, c} and {a, c} for entry c, and {e}, {b, e}, {c, e} and {b, c, e} for entry e.Algorithm 1: TD-FP-GrowthInput: a transaction database, with items in each transaction sorted in the lexicographic order, a minimum support: minsup. Output: frequent patterns above the minimum support. Method: build the FP-tree; then call mine-tree (∅, H); Procedure mine-tree(X, H)(1) for each entry I (top down order) in H do(2)if H(I) >= minsup, then(3)output IX;(4)create a new header table H I by call buildsubtable(I);(5)mine-tree(IX, H I);Procedure buildsubtable(I)(1) for each node u on the side-link of I do(2)walk up the path from u once do if encounter a J_node v then(3)link v into the side-link of J in H I;(4)count(v) = count(v) + count(u);(5)H I (J) = H I (J) + count(u);Unlike FP-Growth, TD-FP-Growth processes nodes at upper levels before processing those at lower levels. This is important to ensure that any modificationTop Down FP-Growth for Association Rule Mining 337 made at upper levels would not affect lower levels. Indeed, as we process a lower level node, all its ancestor nodes have already been processed. Thus, to obtain the “conditional pattern base” of a pattern (as named in [5]), we simply walk up the paths above the nodes on the current side-link and update the counts on the paths. In this way, we update the count information on these paths “in place” without creating a copy of such paths. As a result, during the whole mining process, no additional conditional pattern bases and sub-trees are built. This turns out to be a big advantage over the bottom-up FP-Growth that has to build a conditional pattern base and sub-tree for each pattern found. Our experiments confirm this performance gain.4 TD-FP-Growth for Association Rule MiningIn this section, we extend TD-FP-Growth for frequent pattern mining to association rule mining. We consider two new strategies for association rule mining problem: push multiple minimum supports and push the confidence constraint.In the discussion below, we assume that items are divided into class items and non-class items. Each transaction contains exactly one class item and several non-class items. We consider only rules with one class item on the right-hand side. Class items are denoted by C1,…,C m.4.1 TD-FP-Growth(M) for Multiple Minimum SupportsIn a ToyotaSale database, suppose that Avalon appears in fewer transactions, say 10%, whereas Corolla appears in more transactions, say 50%. If a large uniform minimum support, such as 40%, is used, the information about Avalon will be lost. On the contrary, if a small uniform minimum support, such as 8%, is used, far too many uninteresting rules about Corolla will be found. A solution is to adopt different minimum supports for rules of different classes.We adopt TD-FP-Growth to mine association rules using non-uniform minimum supports. The input to the algorithm is one minsup i for each class C i, and the minimum confidence minconf. The output is all association rules X ⇒C i satisfying minsup i and minconf. The algorithm is essentially TD-FP-Growth with the following differences. (1) We assume that the class item C i in each transaction is the last item in the transaction. (2) Every frequent pattern XC i must contain exactly one class item C I, where X contains no class item. The support of X is represented by H(I) and the support of XC i is represented by H(i, I). (3) The minimum support for XC i is minsup i.(4) We prune XC i immediately if its confidence computed by by H(i, I)/H(I) is below the minimum confidence.4.2 TD-FP-Growth(C) for Confidence PruningA nice property of the minimum support constraint is the anti-monotonicity: if a pattern is not frequent, its supersets are not frequent either. All existing frequent pattern mining algorithms have used this property to prune infrequent patterns. However, the minimum confidence constraint does not have a similar property. This is the main reason that most association rule mining algorithms ignore the confidence requirement in the step of finding frequent patterns. However, if we choose a proper338 Ke Wang et al.definition of support, we can still push the confidence requirement inside the search of patterns. Let us consider the following modified notion of support.Definition 4.1: The (modified) support of a rule A ⇒ B is count(A).We rewrite the minimum confidence constraint C: count(AB)/count(A)>=minconf to count(AB)>=count(A)*minconf. From the support requirement count(A)>=minsup, we have a new constraint C’: count(AB) >= minsup * minconf. Notice that C’ is anti-monotone with respect to the left-hand side A. Also, C’ is not satisfied, neither is C. This gives rise to the following pruning strategy.Theorem 4.1: (1) If A ⇒B does not satisfy C’, A ⇒ B does not satisfy the minimum confidence either. (2) If A ⇒ B does not satisfy C’, no rule AX ⇒B satisfies the minimum confidence, where X is any set of items.Now, we can use both the minimum support constraint and the new constraint C’to prune the search space: if a rule A ⇒ B fails to satisfy both the minimum support and C’, we prune all rules AX ⇒ B for any set of items X. In this way, the search space is tightened up by intersecting the search spaces of the two constraints.5 Experiment ResultsAll experiments are performed on a 550MHz AMD PC with 512MB main memory, running on Microsoft Windows NT4.0. All programs are written in Microsoft Visual C++ 6.0. We choose several data sets from UC_Irvine Machine Learning Database Repository: /~mlearn/MLRepository.html.dataset# of trans # of itemsper transclass distribution# of distinctitemsDna-train200061C1=23.2%, C2=24.25%, C3= 52.55%240 Connect-46755743C1=65.83%, C2= 24.62%, C3= 9.55%126Forest58101213C1= 36.36%, C2= 48.76%, C3= 6.15%, C4:0.47%, C5= 1.63%, C6= 2.99%, C7= 3.53%15916 Table 5.1 data sets tableTable 5.1 shows the properties for the three data sets. C onnect-4 is the densest, meaning that there are a lot of long frequent patterns. Dna-train comes the next in density. Forest is relatively sparse comparing with the other two data sets, however, it is a large data set with 581012 transactions.5.1 Frequent Pattern MiningIn this experiment, we evaluate the performance gain of TD-FP-Growth on mining frequent patterns. We compare it with Apriori and FP-Growth. Figure 5.1 shows a set of curves on the scalability with respect to different minimum supports (minsup). These experiments show that TD-FP-Growth is the most efficient algorithm for all data sets and minimum supports tested.Top Down FP-Growth for Association Rule Mining 3395.2 TD-FP-Growth for Multiple Minimum SupportsIn this experiment, we evaluate the performance gain of using multiple minimum supports. We compare three algorithms: TD-FP-Growth(M), TD-FP-Growth(U), and Apriori. TD-FP-Growth(U) is TD-FP-Growth(M) in the special case that there is only a single minimum support equal to the smallest minimum supports specified. To specify the minimum support for each class, we multiply a reduction factor to the percentage of each class. For example, if there are two classes in a data set and the percentage of them is 40% and 60%, with the reduction factor of 0.1, the minimum supports for the two classes are 4% and 6% respectively. And the uniform minimum support is set to 4%.As shown in Figure 5.2, TD-FP-Growth(M) scales best for all data sets. Take C onnect-4 as the example. As we try to mine rules for the rare class draw, which occurs in 9.55% of the transactions, the uniform minimum support at reduction factor of 0.95 is 9.55% * 95%, TD-FP-Growth(U) ran for more than 6 hours and generated more 115 million rules. However, with the same reduction factor, TD-FP-Growth(M) generated only 2051 rules and took only 5 seconds. That’s why in Figure 5.2 for C onnect-4, the run time for the two algorithms with uniform minimum support is not included.5.3 TD-FP-Growth for Confidence PruningIn this experiment, we evaluate the effectiveness of confidence pruning. The results are reported in Figure 5.3. TD-FP-Growth(NC) is TD-FP-Growth(C) with confidence pruning turned off. We fix the minimum support and vary the minimum confidence. As shown in Figure 5.3, TD-FP-Growth(C) is more efficient than Apriori and TD-FP-Growth(NC), for all data sets, minimum supports, and minimum confidences tested.340 Ke Wang et al.Figure 5.3 confidence-pruning results—time comparison6 ConclusionIn this paper, we proposed an efficient algorithm for frequent pattern mining, calledTD-FP-Growth . The main advantage of TD-FP-Growth is not building conditional pattern bases and conditional FP-tree as the previously proposed FP-Growth does.We extended this algorithm to mine association rules by applying two new pruning strategies. We studied the effectiveness of TD-FP-Growth and the new pruning strategies on several representative data sets. The experiments show that the proposed algorithms and strategies are highly effective and outperform the previously proposed FP-Growth .Reference[1] R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. ACM-SIGMOD 1993, 207-216.[2] H. Toivonen. Sampling large databases for association rules. VLDB 1996, 134-145.[3] R. Agrawal and S. Srikant. Mining sequential patterns. ICDE 1995, 3-14.[4] J. Han, J. Pei and Y. Yin. Mining Frequent patterns without candidate generation. SIGMOD 2000, 1-12.[5] R. Srikant and R. Agrawal. Mining generalized association rules. VLDB 1995, 407-419.[6] R. Srikant, Q. Vu, and R. Agrawal. Mining association rules with item constraints. VLDB 1996, 134-145.[7] B. Liu, W. Hsu, and Y. Ma. Mining association rules with multiple minimum supports.KDD 1999, 337-341[8] K. Wang, Y. He and J. Han. Mining frequent patterns using support constraints. VLDB 2000, 43-52.。
文献信息:文献标题:A Study of Data Mining with Big Data(大数据挖掘研究)国外作者:VH Shastri,V Sreeprada文献出处:《International Journal of Emerging Trends and Technology in Computer Science》,2016,38(2):99-103字数统计:英文2291单词,12196字符;中文3868汉字外文文献:A Study of Data Mining with Big DataAbstract Data has become an important part of every economy, industry, organization, business, function and individual. Big Data is a term used to identify large data sets typically whose size is larger than the typical data base. Big data introduces unique computational and statistical challenges. Big Data are at present expanding in most of the domains of engineering and science. Data mining helps to extract useful data from the huge data sets due to its volume, variability and velocity. This article presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective.Keywords: Big Data, Data Mining, HACE theorem, structured and unstructured.I.IntroductionBig Data refers to enormous amount of structured data and unstructured data thatoverflow the organization. If this data is properly used, it can lead to meaningful information. Big data includes a large number of data which requires a lot of processing in real time. It provides a room to discover new values, to understand in-depth knowledge from hidden values and provide a space to manage the data effectively. A database is an organized collection of logically related data which can be easily managed, updated and accessed. Data mining is a process discovering interesting knowledge such as associations, patterns, changes, anomalies and significant structures from large amount of data stored in the databases or other repositories.Big Data includes 3 V’s as its characteristics. They are volume, velocity and variety. V olume means the amount of data generated every second. The data is in state of rest. It is also known for its scale characteristics. Velocity is the speed with which the data is generated. It should have high speed data. The data generated from social media is an example. Variety means different types of data can be taken such as audio, video or documents. It can be numerals, images, time series, arrays etc.Data Mining analyses the data from different perspectives and summarizing it into useful information that can be used for business solutions and predicting the future trends. Data mining (DM), also called Knowledge Discovery in Databases (KDD) or Knowledge Discovery and Data Mining, is the process of searching large volumes of data automatically for patterns such as association rules. It applies many computational techniques from statistics, information retrieval, machine learning and pattern recognition. Data mining extract only required patterns from the database in a short time span. Based on the type of patterns to be mined, data mining tasks can be classified into summarization, classification, clustering, association and trends analysis.Big Data is expanding in all domains including science and engineering fields including physical, biological and biomedical sciences.II.BIG DATA with DATA MININGGenerally big data refers to a collection of large volumes of data and these data are generated from various sources like internet, social-media, business organization, sensors etc. We can extract some useful information with the help of Data Mining. It is a technique for discovering patterns as well as descriptive, understandable, models from a large scale of data.V olume is the size of the data which is larger than petabytes and terabytes. The scale and rise of size makes it difficult to store and analyse using traditional tools. Big Data should be used to mine large amounts of data within the predefined period of time. Traditional database systems were designed to address small amounts of data which were structured and consistent, whereas Big Data includes wide variety of data such as geospatial data, audio, video, unstructured text and so on.Big Data mining refers to the activity of going through big data sets to look for relevant information. To process large volumes of data from different sources quickly, Hadoop is used. Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. Its distributed supports fast data transfer rates among nodes and allows the system to continue operating uninterrupted at times of node failure. It runs Map Reduce for distributed data processing and is works with structured and unstructured data.III.BIG DATA characteristics- HACE THEOREM.We have large volume of heterogeneous data. There exists a complex relationship among the data. We need to discover useful information from this voluminous data.Let us imagine a scenario in which the blind people are asked to draw elephant. The information collected by each blind people may think the trunk as wall, leg as tree, body as wall and tail as rope. The blind men can exchange information with each other.Figure1: Blind men and the giant elephantSome of the characteristics that include are:i.Vast data with heterogeneous and diverse sources: One of the fundamental characteristics of big data is the large volume of data represented by heterogeneous and diverse dimensions. For example in the biomedical world, a single human being is represented as name, age, gender, family history etc., For X-ray and CT scan images and videos are used. Heterogeneity refers to the different types of representations of same individual and diverse refers to the variety of features to represent single information.ii.Autonomous with distributed and de-centralized control: the sources are autonomous, i.e., automatically generated; it generates information without any centralized control. We can compare it with World Wide Web (WWW) where each server provides a certain amount of information without depending on other servers.plex and evolving relationships: As the size of the data becomes infinitely large, the relationship that exists is also large. In early stages, when data is small, there is no complexity in relationships among the data. Data generated from social media and other sources have complex relationships.IV.TOOLS:OPEN SOURCE REVOLUTIONLarge companies such as Facebook, Yahoo, Twitter, LinkedIn benefit and contribute work on open source projects. In Big Data Mining, there are many open source initiatives. The most popular of them are:Apache Mahout:Scalable machine learning and data mining open source software based mainly in Hadoop. It has implementations of a wide range of machine learning and data mining algorithms: clustering, classification, collaborative filtering and frequent patternmining.R: open source programming language and software environment designed for statistical computing and visualization. R was designed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand beginning in 1993 and is used for statistical analysis of very large data sets.MOA: Stream data mining open source software to perform data mining in real time. It has implementations of classification, regression; clustering and frequent item set mining and frequent graph mining. It started as a project of the Machine Learning group of University of Waikato, New Zealand, famous for the WEKA software. The streams framework provides an environment for defining and running stream processes using simple XML based definitions and is able to use MOA, Android and Storm.SAMOA: It is a new upcoming software project for distributed stream mining that will combine S4 and Storm with MOA.Vow pal Wabbit: open source project started at Yahoo! Research and continuing at Microsoft Research to design a fast, scalable, useful learning algorithm. VW is able to learn from terafeature datasets. It can exceed the throughput of any single machine networkinterface when doing linear learning, via parallel learning.V.DATA MINING for BIG DATAData mining is the process by which data is analysed coming from different sources discovers useful information. Data Mining contains several algorithms which fall into 4 categories. They are:1.Association Rule2.Clustering3.Classification4.RegressionAssociation is used to search relationship between variables. It is applied in searching for frequently visited items. In short it establishes relationship among objects. Clustering discovers groups and structures in the data.Classification deals with associating an unknown structure to a known structure. Regression finds a function to model the data.The different data mining algorithms are:Table 1. Classification of AlgorithmsData Mining algorithms can be converted into big map reduce algorithm based on parallel computing basis.Table 2. Differences between Data Mining and Big DataVI.Challenges in BIG DATAMeeting the challenges with BIG Data is difficult. The volume is increasing every day. The velocity is increasing by the internet connected devices. The variety is also expanding and the organizations’ capability to capture and process the data is limited.The following are the challenges in area of Big Data when it is handled:1.Data capture and storage2.Data transmission3.Data curation4.Data analysis5.Data visualizationAccording to, challenges of big data mining are divided into 3 tiers.The first tier is the setup of data mining algorithms. The second tier includesrmation sharing and Data Privacy.2.Domain and Application Knowledge.The third one includes local learning and model fusion for multiple information sources.3.Mining from sparse, uncertain and incomplete data.4.Mining complex and dynamic data.Figure 2: Phases of Big Data ChallengesGenerally mining of data from different data sources is tedious as size of data is larger. Big data is stored at different places and collecting those data will be a tedious task and applying basic data mining algorithms will be an obstacle for it. Next we need to consider the privacy of data. The third case is mining algorithms. When we are applying data mining algorithms to these subsets of data the result may not be that much accurate.VII.Forecast of the futureThere are some challenges that researchers and practitioners will have to deal during the next years:Analytics Architecture:It is not clear yet how an optimal architecture of analytics systems should be to deal with historic data and with real-time data at the same time. An interesting proposal is the Lambda architecture of Nathan Marz. The Lambda Architecture solves the problem of computing arbitrary functions on arbitrary data in real time by decomposing the problem into three layers: the batch layer, theserving layer, and the speed layer. It combines in the same system Hadoop for the batch layer, and Storm for the speed layer. The properties of the system are: robust and fault tolerant, scalable, general, and extensible, allows ad hoc queries, minimal maintenance, and debuggable.Statistical significance: It is important to achieve significant statistical results, and not be fooled by randomness. As Efron explains in his book about Large Scale Inference, it is easy to go wrong with huge data sets and thousands of questions to answer at once.Distributed mining: Many data mining techniques are not trivial to paralyze. To have distributed versions of some methods, a lot of research is needed with practical and theoretical analysis to provide new methods.Time evolving data: Data may be evolving over time, so it is important that the Big Data mining techniques should be able to adapt and in some cases to detect change first. For example, the data stream mining field has very powerful techniques for this task.Compression: Dealing with Big Data, the quantity of space needed to store it is very relevant. There are two main approaches: compression where we don’t loose anything, or sampling where we choose what is thedata that is more representative. Using compression, we may take more time and less space, so we can consider it as a transformation from time to space. Using sampling, we are loosing information, but the gains inspace may be in orders of magnitude. For example Feldman et al use core sets to reduce the complexity of Big Data problems. Core sets are small sets that provably approximate the original data for a given problem. Using merge- reduce the small sets can then be used for solving hard machine learning problems in parallel.Visualization: A main task of Big Data analysis is how to visualize the results. As the data is so big, it is very difficult to find user-friendly visualizations. New techniques, and frameworks to tell and show stories will be needed, as for examplethe photographs, infographics and essays in the beautiful book ”The Human Face of Big Data”.Hidden Big Data: Large quantities of useful data are getting lost since new data is largely untagged and unstructured data. The 2012 IDC studyon Big Data explains that in 2012, 23% (643 exabytes) of the digital universe would be useful for Big Data if tagged and analyzed. However, currently only 3% of the potentially useful data is tagged, and even less is analyzed.VIII.CONCLUSIONThe amounts of data is growing exponentially due to social networking sites, search and retrieval engines, media sharing sites, stock trading sites, news sources and so on. Big Data is becoming the new area for scientific data research and for business applications.Data mining techniques can be applied on big data to acquire some useful information from large datasets. They can be used together to acquire some useful picture from the data.Big Data analysis tools like Map Reduce over Hadoop and HDFS helps organization.中文译文:大数据挖掘研究摘要数据已经成为各个经济、行业、组织、企业、职能和个人的重要组成部分。
AbstractSeveral algorithms have been proposed for finding the “best,” “opti-mal,” or “most interesting” rule(s) in a database according to a vari-ety of metrics including confidence, support, gain, chi-squared value, gini, entropy gain, laplace, lift, and conviction. In this paper, we show that the best rule according to any of these metrics must reside along a support/confidence border. Further, in the case of conjunctive rule mining within categorical data, the number of rules along this border is conveniently small, and can be mined effi-ciently from a variety of real-world data-sets. We also show how this concept can be generalized to mine all rules that are best according to any of these criteria with respect to an arbitrary subset of the population of interest. We argue that by returning a broader set of rules than previous algorithms, our techniques allow for improved insight into the data and support more user-interaction in the optimized rule-mining process.1. IntroductionThere are numerous proposals for mining rules from data. Some are constraint-based in that they mine every rule satisfying a set of hard constraints such as minimum support or confidence (e.g. [1,2,6]). Others are heuristic in that they attempt to find rules that are predictive, but make no guarantees on the predictiveness or the completeness of the returned rule set (e.g. decision tree and covering algorithms [9,15]). A third class of rule mining algorithms, which are the subject of this paper, identify only the most interesting, or optimal, rules according to some interestingness metric [12,18,20,24]. Optimized rule miners are particularly useful in domains where a constraint-based rule miner produces too many rules or requires too much time.It is difficult to come up with a single metric that quantifies the “interestingness” or “goodness” of a rule, and as a result, several different metrics have been proposed and used. Among them are confidence and support [1], gain [12], variance and chi-squared value [17,18], entropy gain [16,17], gini [16], laplace [9,24], lift [14] (a.k.a. interest [8] or strength [10]), and conviction [8]. Several algorithms are known to efficiently find the best rule (or a close approximation to the best rule [16]) according to a specific one of these metrics [12,18,20,24]. In this paper, we show that a single yet simple concept of rule goodness captures the best rules according to any of them. This concept involves a partial order on rules defined in terms of both rule support and confidence. We demonstrate that the set of rules that are optimal according to this partial order includes all rules that are best according to any of the above metrics, even given arbitrary minimums on support and/or confidence.In the context of mining conjunctive association rules, we present an algorithm that can efficiently mine an optimal set according to this partial order from a variety of real-world data-sets. For example, for each of the categorical data-sets from the Irvine machine learning repository (excepting only connect-4), this algorithm requires less than 30 seconds on a 400Mhz Pentium-II class machine. Specifying constraints such as minimum support or confidence reduces execution time even further. While optimizing according to only a single interestingness metric could sometimes require less overhead, the approach we propose is likely to be advantageous since it supports an interactive phase in which the user can browse the optimal rule according to any of several interestingness metrics. It also allows the user to interactively tweak minimums on support and confidence. Witnessing such effects with a typical optimized rule miner requires repeated mining runs, which may be impractical when the database is large.Another need for repeated invocations of an optimized rule miner arises when the user needs to gain insight into a broader population than what is already well-characterized by previously discovered rules. We show how our algorithm can be generalized to produce every rule that is optimal according to any of the previously mentioned interestingness metrics, and additionally, with respect to an arbitrary subset of the population of interest. Because data-mining is iterative and discovery-driven, identifying several good rules up-front in order to avoid repeatedly querying the database reduces total mining time when amortized over the entire process [13].2. Preliminaries2.1 Generic Problem StatementA data-set is a finite set of records. For the purpose of this paper, a record is simply an element on which we apply boolean predicates called conditions. A rule consists of two conditions called the antecedent and consequent, and is denoted as where is the antecedent and the consequent. A rule constraint is a boolean predicate on a rule. Given a set of constraints , we say that a rule satisfies the constraints in if every constraint in evaluates to true given . Some common examples of constraints are item constraints [22] and minimums on support and confidence [1]. The input to the problem of mining optimized rules is a -tuplewhere:• is a finite set of conditions;• is a data-set;• is a total order on rules;• is a condition specifying the rule consequent;• is a set of constraints on rules.When mining an optimal disjunction, we treat a set of conditions as a condition itself that evaluates to true if and only if one or more of the conditions within evaluates to true on the given record. When mining an optimal conjunction, we treat as a condition that evaluates to true if and only if every condition within evaluates to true on the given record. For both cases, if is empty then it always evaluates to true. Algorithms for mining optimal conjunctions and disjunctions differ significantly in theirA C→ACNr N Nr5U D C N,,≤,,〈〉UD≤CNA U⊆AAA AMining the Most Interesting RulesRoberto J. Bayardo Jr.IBM Almaden Research Center/cs/people/bayardo/ bayardo@Rakesh AgrawalIBM Almaden Research Center /u/ragrawal/ ragrawal@Appears in Proc. of the Fifth ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining, 145-154, 1999.details, but the problem can be formally stated in an identical manner 1:P ROBLEM (O PTIMIZED R ULE M INING ): Find a set such that (1) satisfies the input constraints, and(2) there exists no set such that satisfies the inputconstraints and .Any rule whose antecedent is a solution to an instance of the optimized rule mining problem is said to be -optimal (or just optimal if the instance is clear from the context). For simplicity, we sometimes treat rule antecedents (denoted with and possibly some subscript) and rules (denoted with and possibly some subscript) interchangeably since the consequent is always fixed and clear from the context.We now define the support and confidence values of rules. These values are often used to define rule constraints by bounding them above a pre-specified value known as minsup and minconf respectively [1], and also to define total orders for optimization [12,20]. The support of a condition is equal to the number of records in the data-set for which evaluates to true, and this value is denoted as . The support of a rule , denoted similarly as , is equal to the number of records in the data-set for which both and evaluate to true.2 The antecedent support of a rule is the support of its antecedent alone. The confidence of a rule is the probability with which the consequent evaluates to true given that the antecedent evaluates to true in the input data-set, computed as follows:2.2 Previous Algorithms for the Optimized RuleMining ProblemMany previously proposed algorithms for optimized rule mining solve specific restrictions of the optimized rule mining problem.For example, Webb [24] provides an algorithm for mining an optimized conjunction under the following restrictions:• contains an existence test for each attribute/value pair appear-ing in a categorical data-set outside a designated class column;• orders rules according to their laplace value (defined later);• is empty.Fukuda et al. [12] provide algorithms for mining an optimized dis-junction where:• contains a membership test for each square of a grid formed by discretizing two pre-specified numerical attributes of a data-set (a record is a member of a square if its attribute values fall within the respective ranges);• orders rules according to either confidence, antecedent sup-port, or a notion they call gain (also defined later);• includes minimums on support or confidence, and includes one of several possible “geometry constraints” that restrict the allowed shape formed by the represented set of grid squares;Rastogi and Shim [20] look at the problem of mining an optimized disjunction where:• includes a membership test for every possible hypercube defined by a pre-specified set of record attributes with eitherordered or categorical domains;• orders rules according to antecedent support or confidence;• includes minimums on antecedent support or confidence, a maximum on the number of conditions allowed in the ante-cedent of a rule, and a requirement that the hypercubes corre-sponding to the conditions of a rule are non-overlapping.In general, the optimized rule mining problem, whether conjunctive or disjunctive, is NP-hard [17]. However, features of a specific instance of this problem can often be exploited to achieve tractability. For example, in [12], the geometry constraints are used to develop low-order polynomial time algorithms. Even in cases where tractability is not guaranteed, efficient mining in practice has been demonstrated [18,20,24]. The theoretical contributions in this paper are conjunction/disjunction neutral. However, we focus on the conjunctive case in validating the practicality of these results through empirical evaluation.2.3 Mining Optimized Rules under Partial OrdersWe have carefully phrased the optimized rule mining problem so that it may accommodate a partial order in place of a total order.With a partial order, because some rules may be incomparable,there can be several equivalence classes containing optimal rules.The previous problem statement requires an algorithm to identify only a single rule from one of these equivalence classes. However,in our application, we wish to mine at least one representative from each equivalence class that contains an optimal rule. To do so, we could simply modify the previous problem statement to find all optimal rules instead of just one. However, in practice, the equivalence classes of rules can be large, so this would be unnecessarily inefficient. The next problem statement enforces our requirements specifically:P ROBLEM (P ARTIAL -O RDER O PTIMIZED R ULE M INING ): Find a set of subsets of such that:(1) every set in is optimal as defined by the optimized rule mining problem.(2) for every equivalence class of rules as defined by the partial order, if the equivalence class contains an optimal rule, then exactly one member of this equivalence class is within .We call a set of rules whose antecedents comprise a solution to an instance of this problem an -optimal set. An -optimal rule is one that may appear in an -optimal set.2.4 MonotonicityThroughout this paper, we exploit (anti-)monotonicity properties of functions. A function is said to be monotone (resp. anti-monotone) in if implies that (resp.). For example, the confidence function, which is defined in terms of rule support and antecedent support, is anti-monotone in antecedent support when rule support is held fixed.3. SC-Optimality3.1 DefinitionConsider the following partial order on rules. Given rules and , if and only if:•, or •. Additionally, if and only if and .An -optimal set where contains this partial order is depicted in Figure 1. Intuitively, such a set of rules defines a support-confidence border above which no rule that satisfies the input constraints can fall.1Algorithms for mining optimal disjunctions typically allow a single fixed conjunctive condition without complications, e.g. see [20]. We ignore this issue for simplicity of presentation.2This follows the original definition of support as defined in [1]. The reader is warned that in the work of Fukuda et. al. [14] and Rastogi and Shim [20](who are careful to note the same discrepancy), the definition of support corresponds to our notion of antecedent support.A 1U ⊆A 1A 2U ⊆A 2A 1A 2<A C →I I A r A A sup A ()A C →sup A C →()A C conf A C →()sup A C →()sup A ()----------------------------=U ≤N U ≤N U ≤N k O U A O O I I I I f x ()x x 1x 2<f x 1()f x 2()≤f x 1()f x 2()≥ sc ≤r 1r 2r 1 sc <r 2sup r 1()sup r 2()conf r 1()conf r 2()<∧≤sup r 1()sup r 2()conf r 1()conf r 2()≤∧<r 1 sc =r 2sup r 1()sup r 2()=conf r 1()conf r 2()=I IConsider also the similar partial order such that if and only if:•,or •.The equivalence condition is the same as before. An optimal set of rules according to this partial order forms a lower border.3.2 Theoretical ImplicationsIn this section, we show that for many total orders intended to rank rules in order of interestingness, we have that, and . We say that any such total order is implied by . This property of a total order is useful due to the following fact:L EMMA 3.1: Given the problem instance such that is implied by , an -optimal rule is containedwithin any -optimal set where .Proof:Consider any rule that is not -optimal (for simplicity we will ignore the presence of constraints in ). Because is non-optimal, there must exist some rule that is optimal such that . But then we also have that since isimplied by . This implies that any non--optimal rule is either non--optimal, or it is equivalent to some -optimal rule which resides in an -optimal equivalence class. At least one -optimal equivalence class must therefore contain an -opti-mal rule. Further, because is implied by , every rule in this equivalence class must be -optimal. By definition, an -optimal set will contain one of these rules, and the claim fol-lows.Put simply, mining the upper support/confidence border identifies optimal rules according to several different interestingness metrics.We will show that these metrics include support, confidence,conviction, lift, laplace, gain, and an unnamed interest measure proposed by Piatetsky-Shapiro [19]. If we also mine the lowerborder, metrics such as entropy gain, gini, and chi-squared value are also included. But first, consider the following additional property of total orders implied by :O BSERVATION 3.2: Given instance such that is implied by , and contains a minimum support con-straint and/or a minimum confidence constraint , an -optimal rule is contained within any -optimal set where .The implication of this fact is that we can mine without minimum support and confidence constraints, and the optimal rule given any setting of these constraints will remain in the mined rule set. This allows the user to witness the effects of modifying these constraints without further mining of the data -- a useful fact since the user often cannot determine accurate settings of minimum support or confidence apriori. Minimum support and confidence constraints are quite critical in some applications of optimized rule mining, particularly when the optimization metric is itself support or confidence as in [12] and [20].To identify the interestingness metrics that are implied by , we use the following lemma.L EMMA 3.3: The following conditions are sufficient for establish-ing that a total order defined over a rule value function is implied by partial order :(1) is monotone in support over rules with the same confi-dence, and(2) is monotone in confidence over rules with the same support.Proof:Suppose , then consider a rule where and . Note that by defini-tion and . Now, if a total order has the mono-tonicity properties from above, then and . Since total orders are transitive, we then have that , which establishes the claim. These conditions trivially hold when the rule value function is or . So consider next the Laplace function which is commonly used to rank rules for classification purposes [9,24].The constant is an integer greater than 1 (usually set to the number of classes when building a classification model). Note that if confidence is held fixed to some value , then we can rewrite the Laplace value as below.It is straightforward to show that this expression is monotone in rule support since and . The Laplace function is also monotone in confidence among rules with equivalent support. To see why, note that if support is held constant, in order to raise the function value, we need to decrease the value of the denominator.This decrease can only be achieved by reducing antecedent support, which implies a larger confidence. Note that an optimal set contains the optimized Laplace rule for any valid setting of .This means the user can witness the effect of varying on the optimal rule without additional mining of the database.The gain function of Fukuda et al. [12] is given above, where is a fractional constant between 0 and 1. If confidence is held fixed at , then this function is equal to , which iss c ¬≤r 1 s c ¬<r 2sup r 1()sup r 2()conf r 1()conf r 2()>∧≤sup r 1()sup r 2()conf r 1()conf r 2()≥∧<SupportFigure 1. Upper and lower support-confidence borders. t ≤r 1 sc<r 2r 1 t ≤⇒r 2r 1 sc =r 2r 1⇒ t =r 2 t ≤ sc≤I U D t ≤C N ,,,,〈〉= t ≤ sc ≤I I sc I sc U D sc≤C N ,,,,〈〉=r 1I sc N r 1r 2r 1 sc <r 2r 1 t ≤r 2 t ≤ sc≤I sc I I I sc I sc I t = sc =I I sc sc ≤I U D t ≤C N ,,,,〈〉= t ≤ sc ≤N n s n c I I sc I sc U D sc ≤C N n s n c ,{}}–,,,,〈〉= sc ≤ t ≤f r () sc ≤f r ()f r ()r 1 sc<r 2r sup r 1()sup r ()=conf r 2()conf r ()=r 1 sc ≤r r sc≤r 2r 1 t ≤r r t ≤r 2r 1 t ≤r 2 sup r ()conf r ()laplace A C →()sup A C →()1+sup A ()k+-------------------------------------=k c laplace r ()sup r ()1+sup r ()c ⁄k+-----------------------------=k 1>c 0≥k k gain A C →()sup A C →()θsup A ()×–=θc sup r ()1Θc ⁄–()trivially monotone in support as long as . We can ignore the case where if we assume there exists any rule satisfying the input constraints such that . This is because for any pair of rules and such that and ,we know that irrespective of their support.Should this assumption be false, the gain criteria is optimized by any rule with zero support should one exist (e.g. in the conjunctive case, one can simply add conditions to a rule until its support drops to zero).The gain function is monotone in confidence among rules with equivalent support for reasons similar to the case of the Laplace function. If support is held constant, then an increase in gain implies a decrease in the subtractive term. The subtractive term can be decreased only by reducing antecedent support, which implies a larger confidence. Note that, like from the Laplace function,after identifying the optimal set of rules, the user can vary and view its effect on the optimal rule without additional mining.Another interestingness metric that is identical to gain for a fixed value of was introduced by Piatetsky-Shapiro [19]:Consider next conviction [8], which was framed in [6] as afunction of confidence:Conviction is obviously monotone in confidence since confidence appears in a subtractive term within the denominator. It is also unaffected by variations in rule support if confidence is held constant, which implies monotonicity.Lift, a well-known statistical measure that can be used to rank rules in IBM’s Intelligent Miner [14] (it is also known as interest [8] and strength [10]), can also be framed as a function of confidence [6]:Like conviction, lift is obviously monotone in confidence andunaffected by rule support when confidence is held fixed.The remaining interestingness metrics, entropy gain, gini, and chi-squared value, are not implied by . However, we show that the space of rules can be partitioned into two sets according to confidence such that when restricted to rules in one set, eachmetric is implied by , and when restricted to rules in the other set, each metric is implied by . As a consequence, the optimal rules with respect to entropy gain, gini, and chi-squared value must reside on either the upper or lower support confidence border. This idea is formally stated by the observation below.O BSERVATION 3.4: Given instance , if implies over the set of rules whose confidence is greater than equal to some value , and implies over the set of rules whose confidence is less than or equal to , then an -optimal rule appears in either (a) any optimal set where , or (b) any -optimal set where .To demonstrate that the entropy gain, gini, and chi-squared values satisfy the requirements put forth by this observation, we need to know when the total order defined by a rule value function is implied by . We use an analog of the Lemma 3.3 for this purpose:L EMMA 3.5: The following conditions are sufficient for establish-ing that a total order defined over a rule value function is implied by partial order :(1) is monotone in support over rules with the same confi-dence, and(2) is anti-monotone in confidence over rules with the same support.In [16], the chi-squared, entropy gain, and gini values of a rule are each defined in terms of a function where and given the rule to which it is applied 3 (definitions appear in Appendix A).These functions are further proven to be convex . Another important property of each of these functions is that they reach their minimum at any rule whose confidence is equal to the “expected”confidence [17]. More formally, is minimum when where and . To prove our claims, we exploit two properties of convex functions from [11]:(1) Convexity of a function implies that for an arbitrary dividing point of the line segment between two points and , we have .4(2) A convex function over a given region must be continuous at every point of the region’s relative interior.The next two lemmas show that a convex function which is minimum at has the properties required by Observation 3.4, where from the observation is set to the expected confidence value.L EMMA 3.6: For a convex function which is minimum at , is (1) monotone in for fixed , so long as , and (2) monotone in when for any constant .Proof:For case (1), when is fixed to a constant , for represents a horizontal line segment that extends from point leftward. The value of is clearly anti-monotone in . Because is also anti-monotone in in this region as a consequence of its con-vexity, it follows that is monotone in 5.For case (2), assume the claim is false. This implies there exists a vector defined by for some constant along which is not monotone in . That is, there exist two points and along where yet (see Figure 2). If were defined to be minimum at , then this would contradict the fact that is convex. But since as well as are undefined at this point, another argument is required. Consider then some sufficiently small non-zero value such that and . Because is convex and continuous inc Θ≥c Θ<r conf r ()Θ≥r 1r 2conf r 1()Θ≥conf r 2()Θ<gain r 1()gain r 2()≥k θΘsup C ()D ⁄=p-s A C →()sup A C →()sup A ()sup C ()D--------------------------------–=conviction A C →()D sup C ()–D 1conf A C →()–()----------------------------------------------------=lift A C →()D conf A C →()sup C ()--------------------------------------= sc≤ sc≤ s c ¬≤I U D t ≤C N ,,,,〈〉= sc≤ t ≤γ s c ¬≤ t ≤γI I sc I sc U D sc ≤C N ,,,,〈〉=I s c ¬I s c ¬U D s c ¬≤C N ,,,,〈〉= s c ¬≤3We are making some simplifications: these functions are actually defined in terms of a vector defining the class distribution after a binary split. We are restricting attention to the case where there are only two classes (and which correspond to and respectively). The binary split in our case is the segmentation of data-set made by testing the antecedent condition of the rule.4This well-known property of convex functions is sometimes given as the definition of a convex function, e.g. [16]. While this property is necessary for convexity, it is not sufficient. The proofs of convexity for gini, entro-py, and chi-squared value in [16] are nevertheless valid for the actual def-inition of convexity since they show that the second derivatives of these functions are always non-negative, which is necessary and sufficient [11].5We are not being completely rigorous due to the bounded nature of the convex region over which is defined. For example, the point may not be within this bounded region since can be no greater than . Verifying that these boundary conditions do not affect the validity of our claims is left as an exercise.t ≤f r () s c ¬≤f r ()f r ()f x y ,()x sup A ()sup A C ∪()–=y sup A C ∪()=A C →C ¬C x y D A f x y ,()conf x y ,()c =conf x y ,()y x y +()⁄=c sup C ()D ⁄=f x y ,()x 3y 3,x 1y 1,x 2y 2,max f x 1y 1,()f x 2y 2,(),()f x 3y 3,()≥f x y ,()conf x y ,()c =γf x y ,()conf x y ,()c =f x y ,()conf x y ,()y conf x y ,()c ≥y conf x y ,()A =A c ≥y Y conf x Y ,()conf x Y ,()c ≥conf x Y ,()c =conf x Y ,()x f x f x Y ,()conf x Y ,()f conf x Y ,()c =x D v conf x y ,()A =A c ≥f y x 1y 1,()x 2y 2,()v f x 1y 1,()f x 2y 2,()>y 1y 2<f 00,()f f v δx 2δ–0≥f x 1y 1,()f x 2δ–y 2,()>fits interior region, such a value of is guaranteed to exist unless , which is a trivial boundary case. Now, consider the line . This line must contain a point such that and are non-negative, and one or both of or is non-zero. But because is convex and minimum at , we have that , which is a contradiction. L EMMA 3.7: For a convex function which is minimum at , is (1) anti-monotone in for fixed , so long as , and (2) monotone in when for any constant .Proof:Similar to the previous. The previous two lemmas and Observation 3.4 lead immediately to the following theorem, which formalizes the fact that mining the upper and lower support-confidence borders identifies the optimal rules according to metrics such as entropy gain, gini, and chi-squared value. Conveniently, an algorithm specifically optimized for mining the upper border can be used without modification to mine the lower border by simply negating the consequent of the given instance, as stated by the subsequent lemma.T HEOREM 3.8: Given instance , if is defined over the values given by a convex function over rules where:(1) and , and (2) is minimum at ,then an -optimal rule appears in either (a) any optimal setwhere , or (b) any -optimal set where .L EMMA 3.9: Given an instance , any -optimal set for (where evaluates to true only when evaluates to false) is also an -optimal set.Proof Idea: Note that . Thus,maximizing the confidence of minimizes the confi-dence of . Before ending this section we consider one practical issue -- that of result visualization. Note that the support-confidence borders as displayed in Figure 1 provide an excellent means by which optimal sets of rules may be visualized. Each border clearly illustrates the trade-off between the support and confidence. Additionally, one can imagine the result visualizer color-coding points along these borders that are optimal according to the various interestingness metrics, e.g. blue for Laplace value, red for chi-squared value,green for entropy gain, and so on. The result of modifying minimum support or confidence on the optimal rules could be displayed in real-time as the user drags a marker along either axisin order to specify a minimum bound.3.3 Practical Implications for Mining Optimal Con-junctionsIn this section we present and evaluate an algorithm that efficientlymines an optimal set of conjunctions according to (and due to Lemma 3.9) from many real-world categorical data-sets,without requiring any constraints to be specified by the user. We also demonstrate that the number of rules produced by this algorithm for a given instance is typically quite manageable -- on the order of a few hundred at most. We address the specific problem of mining optimal conjunctions within categorically valued data, where each condition in is simply a test of whether the given input record contains a particular attribute/value pair,excluding values from a designated class column. Values from the designated class column are used as consequents. While our algorithm requires no minimums on support or confidence, if they are specified, they can be exploited for better performance. Space constraints prohibit a full explanation of the workings of this algorithm, so we highlight only the most important features here.A complete description appears in an extended draft [7].The algorithm we use is a variant of Dense-Miner from [6], which is a constraint-based rule miner suitable for use on large and dense data-sets. In Dense-Miner, the rule mining problem is framed as a set-enumeration tree search problem [21] where each node of the tree enumerates a unique element of . Dense-Miner returns every rule that satisfies the input constraints, which include minimum support and confidence. We modified Dense-Miner to instead maintain only the set of rules that are potentially optimal at any given point during its execution. Whenever a rule is enumerated by a node and found to satisfy the input constraints, it is compared against every rule presently in . If is better than or incomparable to every rule already in according to the partial order, then rule is added to . Also, any rule in that is worse than is removed. Given this policy, assuming the tree enumerates every subset of , upon termination, is an optimal set.Because an algorithm which enumerates every subset would be unacceptably inefficient, we use pruning strategies that greatly reduce the search space without compromising completeness.These strategies use Dense-Miner’s pruning functions (appearing in Appendix B), which bound the confidence and support of any rule that can be enumerated by a descendent of a given node. To see how these functions are applied in our variant of the algorithm,consider a node with support bound and confidence bound .To see if can be pruned, the algorithm determines if there existsa rule in such that , where is some imaginary rule with support and confidence . Given such a rule, if any descendent of enumerates an optimal rule, then it must be equivalent to . This equivalence class is already represented in , so there is no need to enumerate these descendents, and can be pruned.This algorithm differs from Dense-Miner in only two additional ways. First, we allow the algorithm to perform a set-oriented best-first search of the tree instead of a purely breadth-first search.Dense-miner uses a breadth-first search since this limits the number of database passes required to the height of the search tree.In the context of optimized rule mining, a breadth-first strategy can be inefficient because pruning improves as better rules are found,and good rules sometimes arise only at the deeper levels. A pure best-first search requires a database pass for each node in the tree,which would be unacceptable for large data-sets. Instead, we process several of the best nodes (at most 5000 in our implementation) with each database pass in order to reduce theδx 20=x 1y 1,()x 2δ–y 2,(),[]x 3y 3,()x 3y 3x 3y 3f x 3y 3,()f x 1y 1,()f x 2δ–y 2,()≤xyFigure 2. Illustration of case (2) from Lemma 3.6.conf(x,y) = cx 2δ–y 2,()x 2y 2,()x 1y 1,()x 3y 3,()f x y ,()conf x y ,()c =f x y ,()conf x y ,()y conf x y ,()c ≤y conf x y ,()A =A c ≤I U D t ≤C N ,,,,〈〉= t ≤f x y ,()A C →x sup A ()sup A C ∪()–=y sup A C ∪()=f x y ,()conf x y ,()sup C ()D ⁄=I I sc I sc U D sc ≤C N ,,,,〈〉=I s c ¬I s c ¬U D s c ¬≤C N ,,,,〈〉=I s c ¬U D s c ¬≤C N ,,,,〈〉=I sc I sc U D sc ≤C ¬N ,,,,〈〉=C ¬C I s c ¬conf A C ¬→()1conf A C →()–=A C ¬→A C → sc≤ s c ¬≤U 2U R r R r R r R R r U R g s c g r R r i sc≤r r i s c g r R g。