数据挖掘外文翻译参考文献

毕业设计（论文）外文资料翻译系部：计算机科学与技术系专业：计算机科学与技术姓名：学号：外文出处：Proceeding of Workshop on the （用外文写）of Artificial,Hualien,TaiWan,2005不确定性数据挖掘：一种新的研究方向Michael Chau1, Reynold Cheng2, and Ben Kao31：商学院，香港大学，薄扶林，香港2：计算机系，香港理工大学九龙湖校区，香港3：计算机科学系，香港大学，薄扶林，香港摘要由于不精确测量、过时的来源或抽样误差等原因，数据不确定性常常出现在真实世界应用中。

目前，在数据库数据不确定性处理领域中，很多研究结果已经被发表。

我们认为，当不确定性数据被执行数据挖掘时，数据不确定性不得不被考虑在内，才能获得高质量的数据挖掘结果。

我们称之为“不确定性数据挖掘”问题。

在本文中，我们为这个领域可能的研究方向提出一个框架。

同时，我们以UK-means 聚类算法为例来阐明传统K-means算法怎么被改进来处理数据挖掘中的数据不确定性。

1.引言由于测量不精确、抽样误差、过时数据来源或其他等原因，数据往往带有不确定性性质。

特别在需要与物理环境交互的应用中，如：移动定位服务[15]和传感器监测[3]。

例如：在追踪移动目标（如车辆或人）的情境中，数据库是不可能完全追踪到所有目标在所有瞬间的准确位置。

因此，每个目标的位置的变化过程是伴有不确定性的。

为了提供准确地查询和挖掘结果，这些导致数据不确定性的多方面来源不得不被考虑。

在最近几年里，已有在数据库中不确定性数据管理方面的大量研究，如：数据库中不确定性的表现和不确定性数据查询。

然而，很少有研究成果能够解决不确定性数据挖掘的问题。

我们注意到，不确定性使数据值不再具有原子性。

对于使用传统数据挖掘技术，不确定性数据不得不被归纳为原子性数值。

再以追踪移动目标应用为例，一个目标的位置可以通过它最后的记录位置或通过一个预期位置（如果这个目标位置概率分布被考虑到）归纳得到。

大数据外文翻译参考文献综述(文档含中英文对照即英文原文和中文翻译)原文：Data Mining and Data PublishingData mining is the extraction of vast interesting patterns or knowledge from huge amount of data. The initial idea of privacy-preserving data mining PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. Privacy-preserving data mining considers the problem of running data mining algorithms on confidential data that is not supposed to be revealed even to the partyrunning the algorithm. In contrast, privacy-preserving data publishing (PPDP) may not necessarily be tied to a specific data mining task, and the data mining task may be unknown at the time of data publishing. PPDP studies how to transform raw data into a version that is immunized against privacy attacks but that still supports effective data mining tasks. Privacy-preserving for both data mining (PPDM) and data publishing (PPDP) has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. One well studied approach is the k-anonymity model [1] which in turn led to other models such as confidence bounding, l-diversity, t-closeness, (α,k)-anonymity, etc. In particular, all known mechanisms try to minimize information loss and such an attempt provides a loophole for attacks. The aim of this paper is to present a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explain their effects on Data Privacy.Although data mining is potentially useful, many data holders are reluctant to provide their data for data mining for the fear of violating individual privacy. In recent years, study has been made to ensure that the sensitive information of individuals cannot be identified easily.Anonymity Models, k-anonymization techniques have been the focus of intense research in the last few years. In order to ensure anonymization of data while at the same time minimizing the informationloss resulting from data modifications, everal extending models are proposed, which are discussed as follows.1.k-Anonymityk-anonymity is one of the most classic models, which technique that prevents joining attacks by generalizing and/or suppressing portions of the released microdata so that no individual can be uniquely distinguished from a group of size k. In the k-anonymous tables, a data set is k-anonymous (k ≥ 1) if each record in the data set is in- distinguishable from at least (k . 1) other records within the same data set. The larger the value of k, the better the privacy is protected. k-anonymity can ensure that individuals cannot be uniquely identified by linking attacks.2. Extending ModelsSince k-anonymity does not provide sufficient protection against attribute disclosure. The notion of l-diversity attempts to solve this problem by requiring that each equivalence class has at least l well-represented value for each sensitive attribute. The technology of l-diversity has some advantages than k-anonymity. Because k-anonymity dataset permits strong attacks due to lack of diversity in the sensitive attributes. In this model, an equivalence class is said to have l-diversity if there are at least l well-represented value for the sensitive attribute. Because there are semantic relationships among the attribute values, and different values have very different levels of sensitivity. Afteranonymization, in any equivalence class, the frequency (in fraction) of a sensitive value is no more than α.3. Related Research AreasSeveral polls show that the public has an in- creased sense of privacy loss. Since data mining is often a key component of information systems, homeland security systems, and monitoring and surveillance systems, it gives a wrong impression that data mining is a technique for privacy intrusion. This lack of trust has become an obstacle to the benefit of the technology. For example, the potentially beneficial data mining re- search project, Terrorism Information Awareness (TIA), was terminated by the US Congress due to its controversial procedures of collecting, sharing, and analyzing the trails left by individuals. Motivated by the privacy concerns on data mining tools, a research area called privacy-reserving data mining (PPDM) emerged in 2000. The initial idea of PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. The solutions were often tightly coupled with the data mining algorithms under consideration. In contrast, privacy-preserving data publishing (PPDP) may not necessarily tie to a specific data mining task, and the data mining task is sometimes unknown at the time of data publishing. Furthermore, some PPDP solutions emphasize preserving the datatruthfulness at the record level, but PPDM solutions often do not preserve such property. PPDP Differs from PPDM in Several Major Ways as Follows ：1) PPDP focuses on techniques for publishing data, not techniques for data mining. In fact, it is expected that standard data mining techniques are applied on the published data. In contrast, the data holder in PPDM needs to randomize the data in such a way that data mining results can be recovered from the randomized data. To do so, the data holder must understand the data mining tasks and algorithms involved. This level of involvement is not expected of the data holder in PPDP who usually is not an expert in data mining.2) Both randomization and encryption do not preserve the truthfulness of values at the record level; therefore, the released data are basically meaningless to the recipients. In such a case, the data holder in PPDM may consider releasing the data mining results rather than the scrambled data.3) PPDP primarily “anonymizes” the data by hiding the identity of record owners, whereas PPDM seeks to directly hide the sensitive data. Excellent surveys and books in randomization and cryptographic techniques for PPDM can be found in the existing literature. A family of research work called privacy-preserving distributed data mining (PPDDM) aims at performing some data mining task on a set of private databasesowned by different parties. It follows the principle of Secure Multiparty Computation (SMC), and prohibits any data sharing other than the final data mining result. Clifton et al. present a suite of SMC operations, like secure sum, secure set union, secure size of set intersection, and scalar product, that are useful for many data mining tasks. In contrast, PPDP does not perform the actual data mining task, but concerns with how to publish the data so that the anonymous data are useful for data mining. We can say that PPDP protects privacy at the data level while PPDDM protects privacy at the process level. They address different privacy models and data mining scenarios. In the field of statistical disclosure control (SDC), the research works focus on privacy-preserving publishing methods for statistical tables. SDC focuses on three types of disclosures, namely identity disclosure, attribute disclosure, and inferential disclosure. Identity disclosure occurs if an adversary can identify a respondent from the published data. Revealing that an individual is a respondent of a data collection may or may not violate confidentiality requirements. Attribute disclosure occurs when confidential information about a respondent is revealed and can be attributed to the respondent. Attribute disclosure is the primary concern of most statistical agencies in deciding whether to publish tabular data. Inferential disclosure occurs when individual information can be inferred with high confidence from statistical information of the published data.Some other works of SDC focus on the study of the non-interactive query model, in which the data recipients can submit one query to the system. This type of non-interactive query model may not fully address the information needs of data recipients because, in some cases, it is very difficult for a data recipient to accurately construct a query for a data mining task in one shot. Consequently, there are a series of studies on the interactive query model, in which the data recipients, including adversaries, can submit a sequence of queries based on previously received query results. The database server is responsible to keep track of all queries of each user and determine whether or not the currently received query has violated the privacy requirement with respect to all previous queries. One limitation of any interactive privacy-preserving query system is that it can only answer a sublinear number of queries in total; otherwise, an adversary (or a group of corrupted data recipients) will be able to reconstruct all but 1 . o(1) fraction of the original data, which is a very strong violation of privacy. When the maximum number of queries is reached, the query service must be closed to avoid privacy leak. In the case of the non-interactive query model, the adversary can issue only one query and, therefore, the non-interactive query model cannot achieve the same degree of privacy defined by Introduction the interactive model. One may consider that privacy-reserving data publishing is a special case of the non-interactivequery model.This paper presents a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explains their effects on Data Privacy. k-anonymity is used for security of respondents identity and decreases linking attack in the case of homogeneity attack a simple k-anonymity model fails and we need a concept which prevent from this attack solution is l-diversity. All tuples are arranged in well represented form and adversary will divert to l places or on l sensitive attributes. l-diversity limits in case of background knowledge attack because no one predicts knowledge level of an adversary. It is observe that using generalization and suppression we also apply these techniques on those attributes which doesn’t need th is extent of privacy and this leads to reduce the precision of publishing table. e-NSTAM (extended Sensitive Tuples Anonymity Method) is applied on sensitive tuples only and reduces information loss, this method also fails in the case of multiple sensitive tuples.Generalization with suppression is also the causes of data lose because suppression emphasize on not releasing values which are not suited for k factor. Future works in this front can include defining a new privacy measure along with l-diversity for multiple sensitive attribute and we will focus to generalize attributes without suppression using other techniques which are used to achieve k-anonymity because suppression leads to reduce the precision ofpublishing table.译文：数据挖掘和数据发布数据挖掘中提取出大量有趣的模式从大量的数据或知识。

数据挖掘技术毕业论文中英文资料对照外文翻译文献综述数据挖掘技术简介中英文资料对照外文翻译文献综述英文原文Introduction to Data MiningAbstract:Microsoft® SQL Server™ 2005 provides an integrated environment for creating and working with data mining models. This tutorial uses four scenarios, targeted mailing, forecasting, market basket, and sequence clustering, to demonstrate how to use the mining model algorithms, mining model viewers, and data mining tools that are included in this release of SQL Server.IntroductionThe data mining tutorial is designed to walk you through the process of creating data mining models in Microsoft SQL Server 2005. The data mining algorithms and tools in SQL Server 2005 make it easy to build a comprehensive solution for a variety of projects, including market basket analysis, forecasting analysis, and targeted mailing analysis. The scenarios for these solutions are explained in greater detail later in the tutorial.The most visible components in SQL Server 2005 are the workspaces that you use to create and work with data mining models. The online analytical processing (OLAP) and data mining tools are consolidated into two working environments: Business Intelligence Development Studio and SQL Server Management Studio. Using Business Intelligence Development Studio, you can develop an Analysis Services project disconnected from the server. When the project is ready, you can deploy it to the server. You can also work directly against the server. The main function of SQL Server Management Studio is to manage the server. Each environment is described in more detail later in this introduction. For more information on choosing between the two environments, see "Choosing Between SQL Server Management Studio and Business Intelligence Development Studio" in SQL Server Books Online.All of the data mining tools exist in the data mining editor. Using the editor you can manage mining models, create new models, view models, compare models, and create predictions basedon existing models.After you build a mining model, you will want to explore it, looking for interesting patterns and rules. Each mining model viewer in the editor is customized to explore models built with a specific algorithm. For more information about the viewers, see "Viewing a Data Mining Model" in SQL Server Books Online.Often your project will contain several mining models, so before you can use a model to create predictions, you need to be able to determine which model is the most accurate. For this reason, the editor contains a model comparison tool called the Mining Accuracy Chart tab. Using this tool you can compare the predictive accuracy of your models and determine the best model.To create predictions, you will use the Data Mining Extensions (DMX) language. DMX extends SQL, containing commands to create, modify, and predict against mining models. For more information about DMX, see "Data Mining Extensions (DMX) Reference" in SQL Server Books Online. Because creating a prediction can be complicated, the data mining editor contains a tool called Prediction Query Builder, which allows you to build queries using a graphical interface. You can also view the DMX code that is generated by the query builder.Just as important as the tools that you use to work with and create data mining models are the mechanics by which they are created. The key to creating a mining model is the data mining algorithm. The algorithm finds patterns in the data that you pass it, and it translates them into a mining model — it is the engine behind the process.Some of the most important steps in creating a data mining solution are consolidating, cleaning, and preparing the data to be used to create the mining models. SQL Server 2005 includes the Data Transformation Services (DTS) working environment, which contains tools that you can use to clean, validate, and prepare your data. For more information on using DTS in conjunction with a data mining solution, see "DTS Data Mining Tasks and Transformations" in SQL Server Books Online.In order to demonstrate the SQL Server data mining features, this tutorial uses a new sample database called AdventureWorksDW. The database is included with SQL Server 2005, and it supports OLAP and data mining functionality. In order to make the sample database available, you need to select the sample database at the installation time in the “Advanced” dialog for component selection.Adventure WorksAdventureWorksDW is based on a fictional bicycle manufacturing company named Adventure Works Cycles. Adventure Works produces and distributes metal and composite bicycles to North American, European, and Asian commercial markets. The base of operations is located in Bothell, Washington with 500 employees, and several regional sales teams are located throughout their market base.Adventure Works sells products wholesale to specialty shops and to individuals through theInternet. For the data mining exercises, you will work with the AdventureWorksDW Internet sales tables, which contain realistic patterns that work well for data mining exercises.For more information on Adventure Works Cycles see "Sample Databases and Business Scenarios" in SQL Server Books Online.Database DetailsThe Internet sales schema contains information about 9,242 customers. These customers live in six countries, which are combined into three regions:North America (83%)Europe (12%)Australia (7%)The database contains data for three fiscal years: 2002, 2003, and 2004.The products in the database are broken down by subcategory, model, and product.Business Intelligence Development StudioBusiness Intelligence Development Studio is a set of tools designed for creating business intelligence projects. Because Business Intelligence Development Studio was created as an IDE environment in which you can create a complete solution, you work disconnected from the server. You can change your data mining objects as much as you want, but the changes are not reflected on the server until after you deploy the project.Working in an IDE is beneficial for the following reasons:The Analysis Services project is the entry point for a business intelligence solution. An Analysis Services project encapsulates mining models and OLAP cubes, along with supplemental objects that make up the Analysis Services database. From Business Intelligence Development Studio, you can create and edit Analysis Services objects within a project and deploy the project to the appropriate Analysis Services server or servers.If you are working with an existing Analysis Services project, you can also use Business Intelligence Development Studio to work connected the server. In this way, changes are reflected directly on the server without having to deploy the solution.SQL Server Management StudioSQL Server Management Studio is a collection of administrative and scripting tools for working with Microsoft SQL Server components. This workspace differs from Business Intelligence Development Studio in that you are working in a connected environment where actions are propagated to the server as soon as you save your work.After the data has been cleaned and prepared for data mining, most of the tasks associated with creating a data mining solution are performed within Business Intelligence Development Studio. Using the Business Intelligence Development Studio tools, you develop and test the datamining solution, using an iterative process to determine which models work best for a given situation. When the developer is satisfied with the solution, it is deployed to an Analysis Services server. From this point, the focus shifts from development to maintenance and use, and thus SQL Server Management Studio. Using SQL Server Management Studio, you can administer your database and perform some of the same functions as in Business Intelligence Development Studio, such as viewing, and creating predictions from mining models.Data Transformation ServicesData Transformation Services (DTS) comprises the Extract, Transform, and Load (ETL) tools in SQL Server 2005. These tools can be used to perform some of the most important tasks in data mining: cleaning and preparing the data for model creation. In data mining, you typically perform repetitive data transformations to clean the data before using the data to train a mining model. Using the tasks and transformations in DTS, you can combine data preparation and model creation into a single DTS package.DTS also provides DTS Designer to help you easily build and run packages containing all of the tasks and transformations. Using DTS Designer, you can deploy the packages to a server and run them on a regularly scheduled basis. This is useful if, for example, you collect data weekly data and want to perform the same cleaning transformations each time in an automated fashion.You can work with a Data Transformation project and an Analysis Services project together as part of a business intelligence solution, by adding each project to a solution in Business Intelligence Development Studio.Mining Model AlgorithmsData mining algorithms are the foundation from which mining models are created. The variety of algorithms included in SQL Server 2005 allows you to perform many types of analysis. For more specific information about the algorithms and how they can be adjusted using parameters, see "Data Mining Algorithms" in SQL Server Books Online.Microsoft Decision TreesThe Microsoft Decision Trees algorithm supports both classification and regression and it works well for predictive modeling. Using the algorithm, you can predict both discrete and continuous attributes.In building a model, the algorithm examines how each input attribute in the dataset affects the result of the predicted attribute, and then it uses the input attributes with the strongest relationship to create a series of splits, called nodes. As new nodes are added to the model, a tree structure begins to form. The top node of the tree describes the breakdown of the predicted attribute over the overall population. Each additional node is created based on the distribution of states of the predicted attribute as compared to the input attributes. If an input attribute is seen tocause the predicted attribute to favor one state over another, a new node is added to the model. The model continues to grow until none of the remaining attributes create a split that provides an improved prediction over the existing node. The model seeks to find a combination of attributes and their states that creates a disproportionate distribution of states in the predicted attribute, therefore allowing you to predict the outcome of the predicted attribute.Microsoft ClusteringThe Microsoft Clustering algorithm uses iterative techniques to group records from a dataset into clusters containing similar characteristics. Using these clusters, you can explore the data, learning more about the relationships that exist, which may not be easy to derive logically through casual observation. Additionally, you can create predictions from the clustering model created by the algorithm. For example, consider a group of people who live in the same neighborhood, drive the same kind of car, eat the same kind of food, and buy a similar version of a product. This is a cluster of data. Another cluster may include people who go to the same restaurants, have similar salaries, and vacation twice a year outside the country. Observing how these clusters are distributed, you can better understand how the records in a dataset interact, as well as how that interaction affects the outcome of a predicted attribute.Microsoft Naïve BayesThe Microsoft Naïve Bayes algorithm quickly builds mining models that can be used for classification and prediction. It calculates probabilities for each possible state of the input attribute, given each state of the predictable attribute, which can later be used to predict an outcome of the predicted attribute based on the known input attributes. The probabilities used to generate the model are calculated and stored during the processing of the cube. The algorithm supports only discrete or discretized attributes, and it considers all input attributes to be independent. The Microsoft Naïve Bayes algorithm produces a simple mining model that can be considered a starting point in the data mining process. Because most of the calculations used in creating the model are generated during cube processing, results are returned quickly. This makes the model a good option for exploring the data and for discovering how various input attributes are distributed in the different states of the predicted attribute.Microsoft Time SeriesThe Microsoft Time Series algorithm creates models that can be used to predict continuous variables over time from both OLAP and relational data sources. For example, you can use the Microsoft Time Series algorithm to predict sales and profits based on the historical data in a cube.Using the algorithm, you can choose one or more variables to predict, but they must be continuous. You can have only one case series for each model. The case series identifies the location in a series, such as the date when looking at sales over a length of several months or years.A case may contain a set of variables (for example, sales at different stores). The Microsoft Time Series algorithm can use cross-variable correlations in its predictions. For example, prior sales at one store may be useful in predicting current sales at another store.Microsoft Neural NetworkIn Microsoft SQL Server 2005 Analysis Services, the Microsoft Neural Network algorithm creates classification and regression mining models by constructing a multilayer perceptron network of neurons. Similar to the Microsoft Decision Trees algorithm provider, given each state of the predictable attribute, the algorithm calculates probabilities for each possible state of the input attribute. The algorithm provider processes the entire set of cases , iteratively comparing the predicted classification of the cases with the known actual classification of the cases. The errors from the initial classification of the first iteration of the entire set of cases is fed back into the network, and used to modify the network's performance for the next iteration, and so on. You can later use these probabilities to predict an outcome of the predicted attribute, based on the input attributes. One of the primary differences between this algorithm and the Microsoft Decision Trees algorithm, however, is that its learning process is to optimize network parameters toward minimizing the error while the Microsoft Decision Trees algorithm splits rules in order to maximize information gain. The algorithm supports the prediction of both discrete and continuous attributes.Microsoft Linear RegressionThe Microsoft Linear Regression algorithm is a particular configuration of the Microsoft Decision Trees algorithm, obtained by disabling splits (the whole regression formula is built in a single root node). The algorithm supports the prediction of continuous attributes.Microsoft Logistic RegressionThe Microsoft Logistic Regression algorithm is a particular configuration of the Microsoft Neural Network algorithm, obtained by eliminating the hidden layer. The algorithm supports the prediction of both discrete andcontinuous attributes.)中文译文数据挖掘技术简介摘要：微软® SQL Server™2005中提供用于创建和使用数据挖掘模型的集成环境的工作。

Applied intelligence, 2005, 22,47-60.一种用于零售银行客户流失分析的数据挖掘方法作者：胡晓华作者单位：美国费城卓克索大学信息科学学院摘要在金融服务业中解除管制，和新技术的广泛运用在金融市场上增加了竞争优势。

每一个金融服务公司的经营策略的关键是保留现有客户，和挖掘新的潜在客户。

数据挖掘技术在这些方面发挥了重要的作用。

在本文中，我们采用数据挖掘方法对零售银行客户流失进行分析。

我们讨论了具有挑战性的问题，如倾向性数据、数据按时序展开、字段遗漏检测等，以及一项零售银行损失分析数据挖掘任务的步骤。

我们使用枚举法作为损失分析的适当方法，用枚举法比较了决策树，选择条件下的贝叶斯网络，神经网络和上述分类的集成的数据挖掘模型。

一些有趣的调查结果被报道。

而我们的研究结果表明，数据挖掘技术在零售业银行中的有效性。

关键词数据挖掘分类方法损失分析1.简介在金融服务业中解除管制，和新技术的广泛运用在金融市场上增加了竞争优势。

每一个金融服务公司经营策略的关键是保留现有客户，和挖掘新的潜在客户。

数据挖掘技术在这些方面中发挥了重要的作用。

数据挖掘是一个结合商业知识，机器学习方法，工具和大量相关的准确信息的反复过程，使隐藏在组织中的企业数据的非直观见解被发现。

这个技术可以改善现有的进程，发现趋势和帮助制定公司的客户和员工的关系政策。

在金融领域，数据挖掘技术已成功地被应用。

•谁可能成为下两个月的流失客户？•谁可能变成你的盈利客户？•你的盈利客户经济行为是什么？•什么产品的不同部分可能被购买？•不同的群体的价值观是什么？•不同部分的特征是什么和每个部分在个人利益中扮演的角色是什么？在本论文中，我们关注的是应用数据挖掘技术来帮助分析零售银行损失分析。

损失分析的目的是确定一组高流失率的客户，然后公司可以控制市场活动来改变所需方向的行为（改变他们的行为，降低流失率）。

在直接营销活动的数据挖掘中，每一个目标客户是无利可图的，无效的，这个概念很容易被理解。

Based on Apriori algorithm Data mining1.Over the valuable hideaway event in the huge database, and performs to analyze Data Mining outlineAlong with take the computer and the network as representative of information technology's development, more and more enterprises, the official organization, educational institution and the scientific research unit has achieved information digitized processing. The information content unceasing growth in the database ask the data memory, the management and the analysis a higher request.One side, the progress of data collection tool enable the humanity to have the huge data quantity, facing assumed the detonation growth of the data, the people need some new tools which could automate transforms the data into the valuable information and the knowledge. Thus, the data mining becomes a new research hot spot domain.On the other hand, along with the data bank technology rapid development and the data management system universal promotion, the data which the people accumulate also day by day .In the sharp increase data also possibility hide many important informations , people hope to make a higher level analysis of the held information, in order to used these data better.The Data Mining (Data Mining)is a new technology which excavates the concealment, formerly unknown, the latent value knowledge to the decision-making from the mass datas. The data mining is a technology that devotes to the data analysis, the understanding and the revelation data interior implication knowledge, it will become one of future information technology application profitable targets. It is likely to other new technical development course, the data mining technology also must after the concept propose, the concept accepts, the widespread research and the exploration, gradually applies and massive applies stages.The data mining technology and the daily life relations already become more and more close. We must face the pointed advertisement every day, the commercial sector reduce the cost through the data mining technology to enhance the efficiency. The data mining opponent worried the data miningobtains the information is threatens people's privacy for the price. Using the data mining might obtain some population statistics information which is unknown before and hideaway in the customer data.The people grow day by day regarding the data mining technology in certain domain application interest, for example cheat examination, suspect identification as well as latent terrorist forecast.The data mining technology may help the people to withdraw from the database correlation data is interested the knowledge, the rule or the higher level information, and may help the people to analyze them from the varying degree, thus may use in the database effectively the data. Not only the data mining technology might use in describing the past data developing process, further also will be able to forecast the future tendency.The data mining technology classified method are very many, according to the data mining duty, may divide into the connection rule excavation, the data class rule excavation, the cluster rule excavation, the dependent analysis and the dependent model discovered, as well as the concept description, the deviation analyze, the trend analysis and the pattern analysis and so on; According to the database which excavates looked that, may divide into the relations database, the object-oriented database, the space database, the time database, the multimedia databases and the different configuration database and so on; According to technical classification which uses, may divide into the artificial neural networks, the decision tree, the genetic algorithm, the neighborhood principle and may the vision and so on.The data mining process by the determination excavation object, the data preparation, the model establishment, the data mining, the result analysis indicates generally and excavates applies these main stages to be composed. The data mining may describe for these stages repeatedly the process.The data mining needs to process the question, is dische gain has the significance information, induces the useful structure, carries on policy-making as the enterprise the basis. Its application is extremely widespread, so long as this industry has the analysis value and the demanddatabase, all may carry on using the Mining tool has the goal excavating analysis. The common application case occurs much at the retail trade, the manufacturing industry, the financial finance insurance, the communication and the medical service.The data mining technology may help the people to withdraw from the database correlation data centralism is interested the knowledge, the rule or the higher level information, and may help the people to analyze them from the varying degree, thus may use in the database effectively the data. Not only the data mining technology might use in describing the past data developing process, further also will be able to forecast the future tendency. In view of this, we study the data mining to have the significance.But the data mining is only a tool, is not multi-purpose, it may discover some potential users, but why can't tell you, also cannot guarantee these potential users become the reality. The data mining success request to expected solves the question domain to have the profound understanding, understands the data, understood its process, can discover the reasonable explanation to the data mining result.2 The connection ruleThe connection rule is refers to between the mass data mean terms collection the interesting connection or the correlation relation. Along with the data accumulation, many field public figures regarding excavate the connection rule from theirs database more and more to be interested. Records from the massive commercial business discovered the interesting incidence relation, may help many commercial decisions-making the formulation.The connection rule discovery initial form is retail merchant's shopping blue analysis, the shopping blue analysis is through discovered the customer puts in its goods blue the different commodity, namely the different between relation, analyzes customer's purchase custom. Through understood which commodities also are purchased frequently by the customer, the analysis obtains between the commodity connection, this kind of connection discovery may help the retail merchant formulation marketing strategy. Theshopping blue analysis model application is may help manager to design the different store layout. One kind of strategy is: Together purchases frequently the commodity may place near somewhat, in order to further stimulate these commodities to sell together. For example, if the customer purchases the computer also to favor simultaneously purchases the financial control software, if then places the hardware exhibits near to the software, possibly is helpful in increases the two the sale. Another kind of strategy is: Place separately the hardware and the software in the store both sides, this possible to induce to purchase these commodities a customer group to choose other commodities. Certainly, the shopping blue analysis is connected the rule discovery the initial form, quite simple. The connection rule discovered the research and the application in unceasingly are also developing. For example, if some food shop through the shopping basket analysis knew “the majority of customers can simultaneously purchase the bread and the milk in a shopping”, then this food shop has the possibility through the reduction promotion bread simultaneously to enhance the bread and the milk sales volume.For example Again, if some children good store through the shopping basket analysis knew “the majority of customers can simultaneously purchase the powdered milk and the urine piece in a shopping”, then this children good store through lays aside separately the powdered milk and the urine piece in the close far place, the middle laying aside some other commonly used child thing, possibly induces the customer when the purchase powdered milk and the urine piece a group purchases other commodities.Digs the stubborn connection rule in business set mainly to include two steps: （1）Discovers all frequent item of collections because the frequent item of collection is the support is bigger than is equal to the smallest support threshold value an item of collection, therefore is composed by frequent item of collection all items the connection rule support is also bigger than is equal to the smallest support threshold value.(2) Has the strong connection rule tohave the strong connection rule is bigger than in all supports was equal to the smallest support threshold value in the connection rule, discovers all confidences to be bigger than is equal to the smallest confidence threshold value the connection rule. In the above two steps, the key is the first step, its efficiency influence entire excavation algorithm efficiency.3 the Apriori algorithmFrequent item of collection: If an item of collection satisfies the smallest support, namely if the item of collection appearance frequency is bigger than or is equal to min_sup (smallest support threshold value) with business database D in business total product, then calls this item of collection the frequent item of collection (Freqent Itemset), abbreviation frequent collection.The frequent k- item of collection set records is usually L K.The Apriori algorithm also called Breadth First or Level the Wise algorithm, proposed by Rakesh Agrawal and Rnamakrishnan Srekant in 1994, it is the present frequent collection discovery algorithm core.The Apriori algorithm uses one kind of being called as cascade search the iterative method, a frequent k- item of collection uses in searching a frequent (k+1)- item of collection.First, discovers the frequent 1- item of collection the set, this set records makes L1, L1 to use in discovering the frequent 2- item of collection set L2, but L2 uses in discovering L3, continue like this, until cannot find a frequent k- item of collection.Looks for each LK to need to scan a database.The connection rule excavation algorithm decomposition is two sub-questions: (1) Extracts in D to satisfy smallest support min_sup all frequent collections; (2) use frequent collection production satisfies smallest confidence level min_conf all connection rule.The first question is the key of this algorithm, the Apriori algorithm solves this problem based on the frequent collection theory recursion method.。

Jilin Province’s population growth and energy consumption analysisMajor StatisticsStudent No. 0401083710Name Niu FukuanJilin Province’s population growth andenergy consumption analysis[Summary]Since the third technological revolution, the energy has become the lifeline of national economy, while the energy on Earth is limited, so in between the major powers led to a number of oil-related or simply a war for oil. In order to compete on the world's resources and energy control, led to the outbreak of two world wars. China's current consumption period coincided with the advent of high-energy, CNPC, Sinopec, CNOOC three state-owned oil giants have been "going out" to develop international markets, Jilin Province as China's energy output and energy consumption province, is also active in the energy corresponding diplomacy. Economic globalization and increasingly fierce competition in the energy environment, China's energy policy is still there are many imperfections, to a certain extent, affect the energy and population development of Jilin Province, China and even to some extent can be said existing population crisis is the energy crisis.[Keyword]Energy consumption; Population; Growth; Analysis;Data sourceI select data from "China Statistical Yearbook 2009" Jilin Province 1995-2007 comprehensive annual financial data (Table 1). Record of the total population (end) of the annual data sequence {Xt}, mind full of energy consumption (kg of standard coal) annual data sequence {Yt}.Table 1 1995-2007 older and province GDP per capita consumption level of all data2001 127627 16629798.1 11.75686723 16.626706712002 128453 17585215.7 11.76331836 16.682569092003 129227 19888035.3 11.76932583 16.805628872004 129988 21344029.6 11.77519742 16.876282612005 130756 23523004.4 11.78108827 16.973489412006 131448 25592925.6 11.78636662 17.057826532007 132129 26861825.7 11.791534 17.106216721.Timing diagramFirst, the total population of Table 1 (end) of the annual data series {Xt}, full of energy consumption (kg of standard coal) annual data series {Yt} are drawn timing diagram, in order to observe the annual population data series {Xt} and national annual energy consumption data sequence {Yt} is stationary, by EVIEWS software output is shown below.Figure 1 of the total population (end) sequence timing diagramFigure 2 universal life energy consumption (kg of standard coal) sequence timing diagramFigure 1 is a sequence {Xt} the timing diagram, Figure 2 is a sequence {Yt} of the timing diagram.Two figures show both the total population (end) or universal life energy consumption (kg of standard coal) index showed a rising trend, the total population of the annual data series {Xt} and national annual energy consumption data sequence {Yt} not smooth, the two may have long-term cointegration relationship.2. Data smoothing(1)Sequence LogarithmFigures 1 and 2 by the intuitive discovery data sequence {Xt} and {Yt} showed a significant growth trend, a significant non-stationary sequence. Therefore, the total population of first sequence {Xt} and universal life energy consumption (kg of standard coal) {Yt}, respectively for the number of treatment to eliminate heteroscedasticity. That logx = lnXt, logy = lnYt, with a view to the target sequence into the linear trend trend sequence, by EVIEWS software operations, the number of sequence timing diagram, in which the population sequence {logx} timing diagram shown in Figure 3, the full sequence of energy consumption {logy} timing diagram shown in Figure 4.Figure3 Figure 4Figure 3 shows the total population observed sequence {logx} and universal life energy consumption (kg of standard coal) sequence {logy} index trend has been basically eliminated, the two have obvious long-term cointegration relationship, which is the transfer function modeling an important prerequisite. However, the above sequence of numbers is still non-stationary series. Respectively {logx} and {logy} sequence of ADF unit root test (Table 5 and Table 6), the test results as shown below. (2)Unit root testHere we will be on the province's total population and the whole sequence {Xt} energy consumption (kg of standard coal) sequence data {Yt} be the unit root test, the results obtained by Eviews software operation is as follows:Table 2 Of the total population sequence {logx}Obtained from Table 2: Total population sequence data {Xt} of the ADF is -0.784587, significantly larger than the 1% level in the critical test value of -4.3260, the 5% level greater than the critical value of -3.2195 testing, but also greater than 10% level in the critical test value -2.7557, so the total population of the data sequence {logx} {Xt} is a non-stationary series.Table 3 National energy consumption (kg of standard coal) unit root test {logy}Obtained from Table 3: National energy consumption (kg of standard coal) data {Yt} of the ADF is 0.489677, significantly larger than the 1% level in the critical test value of -4.3260, the 5% level greater than the critical test value of -3.2195, but also 10% greater than the critical level test value -2.7557, so the total population of the sequence {logx} data {Yt} is a non-stationary series.(3) Sequence of differentialBecause of the number of time series after still not a smooth sequence, so the need for further logarithm of the total population after the sequence {logx} and after a few of the universal life energy consumption (kg of standard coal) differential sequence data {logY} differential sequences were recorded as {▽logx} and {▽logy}. Are respectively the second-order differential of the total population of the sequence {▽logX} and second-order differential of the national energy consumption (kg of standard coal) sequence data {▽ logy} the ADF unit root test (Table 7 and Table 8), test results the following table.Table 4Table 4 shows that the total population of second-order differential sequence {▽logx} ADF value is -10.6278, apparently less than 1% level in the critical test value of -6.292057, less than the 5% level in the critical test value -4.450425 also 10% less than the level in the critical test value of -3.701534, second-order differential of the total population of the sequence {▽ logx} is a stationary sequence.Table5 5Table 5 shows that the second-order differential universal life energy consumption (kg of standard coal) {▽logy} of the ADF is -6.395029, apparently less than 1% level in the critical test value of -4.4613, less than the 5 % level of the critical test value of -3.2695, but also less than the 10% level the critical value of -2.7822 testing,universal life, second-order differential consumption of energy (kg of standard coal) {▽ logy} is a stationary sequence.3. Cointegration(1)Cointegration regressionCointegration theory in the 1980s there Engle Granger put forward specific, it is from the analysis of non-stationary time series start to explore the non-stationary variable contains the long-run equilibrium relationship between the non-stationary time series modeling provides a new solution.As the population time series {Xt} and universal life energy consumption time series {Yt} are logarithmic, the total population obtained by the analysis of time series {logX} and universal life energy consumption time series {logY} are second-order single whole sequence, so they may exist cointegration relationship. The results obtained by Eviews software operation is as follows:Table 6Obtained from Table 6:D(LNE2)= -0.054819 – 101.8623D(LOGX2)t = (-1.069855) (-1.120827)R2=0.122487 DW=1.593055（2）Check the smoothness of the residual sequenceFrom the Eviews software, get residual sequence analysis:Table 7Residual series unit root testObtained from Table 7: second-order differential value of -5.977460 ADF residuals, significantly less than 1% level in the critical test value -4.6405, less than 5% level in the critical test value of -3.3350, but also less than 10% level in the critical test value of -2.8169. Therefore, the second-order difference of the residual et is a stationary time series sequence. Expressed as follows:D（ET,2）=-0.042260-1.707007D(ET(-1),2)t = （-0.783744）(-5.977460)DW= 1.603022 EG=-5.977460，Since EG =- 5.977460, check the AFG cointegration test critical value table (N = 2, = 0.05, T = 16) received, EG value is less than the critical value, so to accept the original sequence et is stationary assumption. So you can determine the total population and energy consumption of all the people living there are two variables are long-term cointegration relationship.4. ECM model to establishThrough the above analysis, after the second-order differential of the logarithm of the total population time series {▽ logX} and second-order differential of Logarithm of of national energy consumption time series {▽ logY} is a stationary sequence, the second-order differential residuals et is also a stationary series. So that the number of second-order differential of the national energy consumption time series {▽ logY} as the dependent variable, after the second-order differential of the logarithm of the total population time series {▽logX} and second-order differential as residuals et from variable regression estimation, using Eviews software, the following findings:Table 8ECM model resultsTable 8 can be written by the ECM standard regression model, results are as follows:D(logY2)= -0.047266-154.4568D(LNP2) +0.171676D(ET2)t = (-1.469685) (-2.528562) (1.755694)R2= 0.579628 DW=1.760658ECM regression equation of the regression coefficients by a significance test, the error correction coefficient is positive, in line with forward correction mechanism. The estimation results show that the province of everyone's life changes in energy consumption depends not only on the change of the total population, but also on the previous year's total population deviation from the equilibrium level. In addition, the regression results show that short-term changes in the total population of all the people living there is a positive impact on energy consumption. Because short-term adjustment coefficient is significant, it shows that all the people living in JilinProvince annual consumption of energy in its long-run equilibrium value is the deviation can be corrected well.5. ARMA model(1) Model to identifyAfter differential differenced stationary series into stationary time series, after the analysis can be used ARMR model, the choice of using the model of everyone's life before the first stable after the annual energy consumption time series {logY} to estimate the first full life energy consumption sequence {logY} do autocorrelation and partial autocorrelation, the results of the following:Table 9{logy} of the autocorrelation and partial autocorrelation mapObtained from Table 9, the relevant figure from behind, after K = 1 in a random interval, partial autocorrelation can be seen in K = 1 after a random interval. So we can live on national energy consumption to establish the sequence {logY} ARMA (1,1) model, following on the ARMA (1,1) model parameter estimation, which results in the following table:Table 10ARMA (1,1) model parameter estimationTable 10 obtained by the ARMA (1,1) model parameter estimation is given by: D(LNE,2)=0.014184+0.008803D(LNE,2)t-1-0.858461U t-1(2)ARMA (1,2) model testModel of the residuals obtained for white noise test, if the residuals are not white noise sequence, then the need for ARMA (1,2) model for further improvement; if it is white noise process, the acceptance of the original model. ARMA (1,2) model residuals test results are as follows:Table11 ARMA (1,2) model residuals testTable 11 shows, Q statistic P value greater than 0.05, so the ARMA (1,1) model, the residual series is white noise sequence and accept the ARMA (1,1) model. Our whole life to predict changes in energy consumption, the results are as follows:Figure 5 National energy consumption forecast mapJilin Province of everyone's life through the forecast energy consumption, we can see all the people living consumption of energy is rising every year, which also shows that in the future for many years, Jilin Province, universal life energy consumption will be showing an upward trend. And because of the total population and the existence of universal life energy consumption effects of changes in the same direction, so the total population over the next many years, will continue to increase.6. ProblemsBased on the province's total population and the national energy consumption cointegration analysis of the relationship between population and energy consumption obtained between Jilin Province, there are long-term stability of the interaction and mutual promotion of the long-run equilibrium relationship. The above analysis can be more accurate understanding of the energy consumption of Jilin Province, Jilin Province put forward a better proposal on energy conservation. Moment, Jilin Province facing energy problems:(1) The heavy industry still accounts for a large proportion of;(2)The scale of energy-intensive industry, the rapid growth of production ofenergy saving effect;(3)The coal-based energy consumption is still.7.Recommendation:(1) Population control, and actively cooperate with the national policy of family planning, ease the pressure on the average population can consume.(2) Raise awareness of the importance of energy saving, the implementation of energy-saving target responsibility system, energy efficiency are implemented.Conscientiously implement the State Council issued the statistics of energy saving, monitoring and evaluation program of the three systems. Strict accountability.(3) Speed up industrial restructuring and transformation of economic development. Speed up industrial restructuring and transformation of economic development, to overcome the resource, energy and other bottlenecks, and take the high technological content, good economic returns, low resources consumption, little environmental pollution and human resources into full play to the new industrialization path.(4) Should pay attention to quality improvement and optimization of the structure, so that the final implementation of the restructuring to improve the overall quality of industrial and economic growth, quality and efficiency up.(5) To enhance the development and promotion of energy-saving technologies, strengthen energy security, promotion of renewable energy, clean energy.Adhere to technical progress and the deepening of reform and opening up the combination. To enhance the independent innovation capability as the adjustment of industrial structure, changing the growth mode of the central link, speed up the innovation system, efforts to address the constraints of the city development major science and technology. Vigorously promote the recycling economy demonstration pilot enterprises to actively carry out comprehensive utilization of resources and renewable resources recycling. And actively promote solar, wind, biogas, biodiesel and other renewable energy construction.References[1] Wang Yan, Applied time series analysis of the Chinese People's University Press, 2008.12[2] Pang Hao. Econometric Science Press, 2006.1。

Summary of Data Mining TechnologyAbstract: With the development of computer and network technology, it is very easy to obtain relevant information. But for the large number of large-scale data, the traditional statistical methods can not complete the analysis of such data. Therefore, an intelligent, comprehensive application of a variety of statistical analysis, database, intelligent language to analyze large data data "data mining" (Date Mining) technology came into being. This paper mainly introduces the basic concept of data mining and the method of data mining. The application of data mining and its development prospect are also described in this paper.Keywords: data mining; method; application; foreground1 IntroductionWith the rapid development of information technology, the scale of the database has been expanding, resulting in a lot of data. The surge of data is hidden behind a lot of important information, people want to be able to conduct a higher level of analysis in order to make better use of these data. In order to provide decision makers with a unified global perspective, data warehouses are established in many areas. But a lot of data often makes it impossible to identify hidden in which can provide support for decision-making information, and the traditional query, reporting tools can not meet the needs of mining this information. Therefore, the need for a new data analysis technology to deal with large amounts of data, and from the extraction of valuable potential knowledge, data mining (Data Mining) technology came into being. Data mining technology is also accompanied by the development of data warehouse technology and gradually improved.2 Data Mining Technology2.1 Definition of data miningData mining refers to the non-trivial process of automatically extracting useful information hidden in the data from the data set. The information is represented by rules, concepts, rules and patterns. It helps decision makers analyze historical data and current data and discover hidden relationships and patterns to predict future behaviors that may occur. The process of data mining is also called the process of knowledge discovery. It is a kind of interdisciplinary and interdisciplinary subject, which involves the fields of database, artificial intelligence, mathematical statistics, visualization and parallel computing. Data mining is a new information processing technology, its main feature is the database of large amounts of data extraction, conversion, analysis and other modelprocessing, and extract the auxiliary decision-making key data. Data mining is an important technology in KDD (Knowledge Discovery in Database). It does not use the standard database query language (such as SQL) to query, but the content of the query to summarize the pattern and the inherent law of the search. Traditional query and report processing are only the result of the incident, and there is no in-depth study of the reasons for the occurrence of data mining is the main understanding of the causes of occurrence, and with a certain degree of confidence in the future forecast for the decision-making behavior to provide favorable stand by.2.2 Methods of data miningData mining research combines a number of different disciplines in the field of technology and results, making the current data mining methods show a variety of forms. From the perspective of statistical analysis, the data mining models used in statistical analysis techniques are linear and non-linear analysis, regression analysis, logistic regression analysis, univariate analysis, multivariate analysis, time series analysis, recent sequence analysis, and recent Oracle algorithm and clustering analysis and other methods. Using these techniques, you can examine the data in those unusual forms, and then interpret the data using various statistical models and mathematical models to explain the market rules and business opportunities that are hidden behind those data. Knowledge discovery class Data mining technology is a kind of mining technology which is completely different from the statistical analysis class data mining technology, including artificial neural network, support vector machine, decision tree, genetic algorithm, rough set, rule discovery and association order.2.2.1 Statistical methodsTraditional statistics provide a number of discriminant and regression analysis methods for data mining. Commonly used techniques such as Bayesian reasoning, regression analysis, and variance analysis. Bayesian reasoning is the basic principle of correcting the probability distribution of data sets after knowing new information Tools, to deal with the classification of data mining problems, regression analysis used to find an input variable and the relationship between the output variables of the best model, in the regression analysis used to describe a variable trends and other variables of the relationship between the linear regression, There is also a logarithmic regression for predicting the occurrence of certain events. The variance analysis in the statistical method is generally used to analyze the effects of estimating the regression line's performance and the independent variables on the final regression, which is the result of many mining applications One of the powerful tools.2.2.2 Association rulesThe association rule is a simple and practical analysis rule, which describes the law and pattern of some attributes in one thing at the same time, which is one of the most mature and important technologies in data mining. It is made by R. Agrawal et al. First proposed that the most classical association rule mining algorithm is Apriori, which first digs out all frequent itemsets, and then generates association rules from frequent itemsets. Many mining rules of frequent rule sets are It evolved from the evolution of the rules in the field of data mining is widely used in large data sets to find a meaningful relationship between the data, one of the reasons is that it is not only a choice of a dependent variable, the association rules in the data The most typical application of the mining area is the shopping basket analysis. Most association rule mining algorithms can discover all the associated relationships hidden in the mining data, and the amount of association rules is often very large. However, not all the relationships between the attributes obtained through the association are practical. Value, the effective evaluation of these association rules, screening out the user is really interested, meaningful association rules is particularly important.2.2.3 Clustering analysisCluster analysis is based on the criteria associated with the selected samples to be divided into several groups, the same group of samples with high similarity, different groups are different, commonly used techniques have split algorithm, cohesion algorithm, Clustering and incremental clustering. The clustering method is suitable for the internal relationship between the samples, so as to make a reasonable evaluation of the sample structure. In addition, the cluster analysis is also used to detect the isolated points. Sometimes clustering is not intended to get objects together but to make it easier for an object to be separated from other objects. Cluster analysis has been applied to a variety of areas such as economic analysis, pattern recognition, image processing, and especially in business. Clustering analysis can help marketers discover different groups of characteristics that exist in customer groups. The key to clustering analysis In addition to the choice of algorithms, it is the choice of metrics for the sample. The classes that are not derived from the clustering algorithm are effective for decision making. Before applying an algorithm, the clustering trend of the data is usually checked first.2.2.4 Decision tree methodDecision tree learning is a method of approximating discrete objective functions by classifying instances from a root node to a leaf node to classify an instance. The leaf node is the classification of the instance. Each node on the tree illustrates a test of anattribute of the instance, and each subsequent branch of the node corresponds to a possible value of the attribute. The method of sorting the instance is from the root node of the tree, Test the properties specified by this node, and then move down the corresponding branch of the attribute value for the given instance. Decision tree method is to be applied to the classification of data mining.2.2.5 neural networkThe neural network is based on the mathematical model of self-learning, which can analyze a large number of complex data and can complete the extremely complex pattern extraction and trend analysis for human brain or other computer. The neural network can be expressed as guidance The learning can also be a non-guided cluster, whichever is the value entered into the neural network. Artificial neural network is used to simulate the structure of human brain neurons. Based on MP model and Hebb learning rules, three kinds of neural networks are established, which have non-linear mapping characteristics, information storage, parallel processing and global collective action, High degree of self-learning, self-organizing and adaptive ability. The feedforward neural network is represented by the sensor network and BP network, which can be used for classification and prediction. The feedback network is represented by Hopfield network for associative memory and optimization. The self-organizing network is based on ART model, Kohonon The model is represented for clustering.2.2.6 support vector machineSupport vector machine (SVM) is a new machine learning method developed on the basis of statistical learning theory. It is based on the principle of structural risk minimization, as far as possible to improve the learning machine generalization ability, has good promotion performance and good classification accuracy, can effectively solve the learning problem, has become a training multi-layer sensor, RBF An Alternative Method for Neural Networks and Polynomial Neural Networks. In addition, the support vector machine algorithm is a convex optimization problem, the local optimal solution must be the global optimal solution, these features are including the neural network, including other algorithms can not and. Support vector machine can be applied to the classification of data mining, regression, the exploration of unknown things and so on. In addition to the above methods, there are ways to convert data and results into visualization techniques, cloud model methods, and inductive logic programs.In fact, any kind of excavation tool is often based on specific issues to select the appropriate mining method, it is difficult to say which method is good, that method is inferior, but depending on the specific problems.2.3 data mining processFor data mining, we can be divided into three main stages: data preparation, data mining, evaluation and expression of results. The results of the evaluation and expression can also be broken down into: assessment, interpretation model model, consolidation, the use of knowledge. Knowledge discovery in the database is a multi-step process, but also the three stages of the repeated process,2.3.1 Data PreparationKDD processing object is a lot of data, these data are generally stored in the database system, the long-term accumulation of the results. But often not suitable for direct knowledge mining on these data, need to do data preparation, generally including the choice of data (select the relevant data), clean (eliminate noise, data), speculate (estimate missing data), conversion (discrete Data conversion between data and continuous value data, packet classification of data values, calculation combinations between data items, etc.), data reduction (reduction of data volume). These jobs are often prepared when the data warehouse is generated. Data preparation is the first step in KDD. Whether data preparation is good will affect the efficiency and accuracy of data mining and the effectiveness of the final model.2.3.2 Data miningData mining is the most critical step KDD, but also technical difficulties. Most of the research KDD personnel are studying data mining technology, using more technology to have decision tree, classification, clustering, rough set, association rules, neural network, genetic algorithm and so on. Data mining According to the goal of KDD, select the parameters of the corresponding algorithm, analyze the data, and get the model model of the possible model layer knowledge.2.3.3 Results evaluation and expressionEvaluation model: the model model obtained above, there may be no practical significance or no use value, it may not be able to accurately reflect the true meaning of the data, even in some cases is contrary to the facts, so need Evaluate, determine which are valid and useful patterns. Evaluation can be based on years of experience, some models can also be used directly to test the accuracy of the data. This step also includes presenting the pattern to the user in an easy-to-understand manner.Consolidate knowledge: the user understands and is considered to be consistent with the actual and valuable model of the model that forms the knowledge. But also pay attention to the consistency of knowledge to check, with the knowledge obtained before the conflict, contradictory embankment, so that knowledge is consolidated.The use of knowledge: to find knowledge is to use, how to make knowledge can be used is one of the steps of KDD. There are two ways to use knowledge: one is to rely on the relationship or result described by the knowledge itself to support decision-making; the other is to require the use of new data knowledge, which may produce new problems, and Need to further optimize the knowledge. The process of KDD may need to be repeated multiple times. Once each step does not match the expected target, go back to the previous step, re-adjust, and re-execute.3 data mining applicationsThe potential application of data mining is very broad: government management decision-making, business management, scientific research and industrial enterprise decision support and other fields.3.1 Applied in scientific researchFrom the point of view of scientific research methodology, scientific research can be divided into three categories: theoretical science, experimental science and computational science. Computational science is an important symbol of modern science. Computing scientists work with data and analyze a wide variety of experimental or observational data every day. With the use of advanced scientific data collection tools, such as observing satellites, remote sensors, DNA molecular technology, the amount of data is very large, the traditional data analysis tools can not do anything, so there must be a strong intelligent automatic data analysis tools Caixing. Data mining in astronomy has a very famous application system: SKICAT (Sky Image Cataloging andAnalysis Tool). It is a tool developed by the California Institute of Technology's Jet Propulsion Laboratory (a laboratory designed to design a Mars probe rover) and astronomical scientists to help astronomers discover distant quasars. SKICAT is both the first successful data mining application and one of the first successful applications of artificial intelligence in astronomy and space science. Using SKICAT, astronomers have discovered 16 new and distant quasars that help astronomers better study the formation of quasars and the structure of the early universe. The application of data mining in biology is mainly focused on the study of molecular biology, especially genetic engineering. Gene research, there is a well-known international research project - the human genome project.3.2 in the commercial applicationIn the business sector, especially in the retail industry, the use of data mining is more successful. As the MIS system in the commercial use of universal, especially the use of code technology, you can collect a lot of data on the purchase situation, and the amount of data in the surge. The use of data mining technology can provide managers with theright decision-making means, so to promote sales and improve competitiveness is of great help.3.3 in the financial applicationIn the financial sector, the amount of data is very large, banks, securities companies and other transaction data and storage capacity is great. And for credit card fraud, the bank's annual loss is very large. Therefore, you can use data mining to analyze the customer's reputation. Typical financial analysis areas include investment assessment and stock trading market forecasts.3.4 in medical applicationsData mining in the medical application is very wide, from molecular medicine to medical diagnosis, can use data mining means to improve efficiency and efficiency. In the case of drug synthesis, the analysis of the chemical structure of the drug molecule can determine which of the atoms or atomic genes in the drug can play a role in the disease, so that in the synthesis of new drugs, according to the molecular structure of the drug to determine the drug will be possible What kind of disease? Data mining can also be used in industry, agriculture, transportation, telecommunications, military, Internet and other industries. Data mining has a wide range of application prospects, it can be applied to decision support, can also be applied to the database management system (DBMS). Data mining as a tool for decision support and analysis can be used to construct a knowledge base. In DBMS, data mining can be used for semantic query optimization, integrity constraints and inconsistent checks.4 Development Trend of Data MiningDue to the diversity of data, data mining tasks and data mining methods, many challenging topics are proposed for data mining. At the same time, the design of data mining language, efficient and useful data mining methods and system development, interactive and integrated data mining environment, as well as the application of data mining technology to solve large application problems, are currently data mining researchers, systems And the main problems faced by application developers. At present, the development trend of data mining is mainly as follows: application exploration; scalable data mining method; data mining and database system, data warehouse system and Web database system integration; data mining language standardization; visual data mining; Complex mining of new data types; Web mining; data mining in the privacy protection and information security.5 concluding remarksAt present, although the data mining technology has been applied to a certain degree, andachieved remarkable results, but there are still many unresolved problems, such as data preprocessing, mining algorithms, pattern recognition and interpretation, visualization problems. For the business process, the most critical issue of data mining is how to combine the spatial and temporal characteristics of business data, will be excavated out of knowledge, that is, time and space knowledge expression and interpretation mechanism. With the deepening of data mining technology, data mining technology will be applied in a wider range of areas, and achieved more significant results.Reference[1] HAN Jia-wei,KAMBER M. Data Mining Concepts and Technigues [M]. FAN Ming,MENG Xiao-feng,trrnsl. Beijing:China Ma-chine Press,2010. 305-307.(in Chinese)[2] ZHOU Bin,LIU Ya-ping,WU Ouan-yuan. The design and implementations issues of a data mining systems for eIectronic commerce[J]. Computer Engineering,2012,26 (6) :18-20.(in Chinese)[3] WANG Jia-cai,CHEN Oi,ZHAO Jie-yu,etla. VISMiner:An interactive visua I data mining prototyped system [J] . Computer Engi-neering,2003,29 (1) :17-19.(in Chinese)[4] LIU Kan,ZHOU Xiao-zheng,ZHOU Dong-ru. Visua I data mining based on para IIe I coordinates [J]. Computer Engineering and Ap-p Iications,2013,39 (5) : 193-196.(in Chinese)[5] NETZA,CHAUDHURI S,FAYYAD U,et al. Integrating data mining with SOL databases:OLE DB for data mining [A] . Pro 17th Int Conf on Data Engineering [C]. Heide Iberg:IEEE,2001. 379-387.[6] ZHAO Zhi-hong,LUO Bin,CHEN Shi-fu. A structure of data mining system based on data warehouse [J] . Computer App Iications and Software,2012,19 (4) :27-30.(in Chinese)[7] OIAN Wei-ning,WEI Li,WANG Yan,et a I. A data mining system for very Iarge databases [J]. Journa I of Software, 2012, 13 (8) :1540-1545.(in Chinese)[8] Quanyin Zhu，Jin Ding，Yonghua Yin，et al. A HybridApproach for New Products Discovery of Cell PhoneBased on Web Mining[J]. Journal of Information andComputational Science. 2012，9( 16) : 5039－5046.[9]Quanyin Zhu，Pei Zhou，Sunqun Cao，et al. A novel RDB－SW approach for commodities price dynamic trend a-nalysis based on Web extracting[J]. Journal of Digital In-formation Management，2012，10( 4) : 230－235.[10]Quanyin Zhu，Pei Zhou. The System Architecture for theBasic Information of Science and Technology ExpertsBased on Distributed Storage and Web Mining[C]. Pro-ceedings of the International Conference on ComputerScience and Service System，2012: 661－664.数据挖掘技术综述摘要：随着计算机、网络技术的发展，获得有关资料非常简单易行。

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。

数据分析外文文献+翻译文献1：《数据分析在企业决策中的应用》该文献探讨了数据分析在企业决策中的重要性和应用。

研究发现，通过数据分析可以获取准确的商业情报，帮助企业更好地理解市场趋势和消费者需求。

通过对大量数据的分析，企业可以发现隐藏的模式和关联，从而制定出更具竞争力的产品和服务策略。

数据分析还可以提供决策支持，帮助企业在不确定的环境下做出明智的决策。

因此，数据分析已成为现代企业成功的关键要素之一。

文献2：《机器研究在数据分析中的应用》该文献探讨了机器研究在数据分析中的应用。

研究发现，机器研究可以帮助企业更高效地分析大量的数据，并从中发现有价值的信息。

机器研究算法可以自动研究和改进，从而帮助企业发现数据中的模式和趋势。

通过机器研究的应用，企业可以更准确地预测市场需求、优化业务流程，并制定更具策略性的决策。

因此，机器研究在数据分析中的应用正逐渐受到企业的关注和采用。

文献3：《数据可视化在数据分析中的应用》该文献探讨了数据可视化在数据分析中的重要性和应用。

研究发现，通过数据可视化可以更直观地呈现复杂的数据关系和趋势。

可视化可以帮助企业更好地理解数据，发现数据中的模式和规律。

数据可视化还可以帮助企业进行数据交互和决策共享，提升决策的效率和准确性。

因此，数据可视化在数据分析中扮演着非常重要的角色。

翻译文献1标题： The Application of Data Analysis in Business Decision-making The Application of Data Analysis in Business Decision-making文献2标题： The Application of Machine Learning in Data Analysis The Application of Machine Learning in Data Analysis文献3标题： The Application of Data Visualization in Data Analysis The Application of Data Visualization in Data Analysis翻译摘要：本文献研究了数据分析在企业决策中的应用，以及机器研究和数据可视化在数据分析中的作用。

什么是数据挖掘？简单地说，数据挖掘是从大量的数据中提取或“挖掘”知识。

该术语实际上有点儿用词不当。

注意，从矿石或砂子中挖掘黄金叫做黄金挖掘，而不是叫做矿石挖掘。

这样，数据挖掘应当更准确地命名为“从数据中挖掘知识”，不幸的是这个有点儿长。

“知识挖掘”是一个短术语，可能它不能反映出从大量数据中挖掘的意思。

毕竟，挖掘是一个很生动的术语，它抓住了从大量的、未加工的材料中发现少量金块这一过程的特点。

这样，这种用词不当携带了“数据”和“挖掘”，就成了流行的选择。

还有一些术语，具有和数据挖掘类似但稍有不同的含义，如数据库中的知识挖掘、知识提取、数据/模式分析、数据考古和数据捕捞。

许多人把数据挖掘视为另一个常用的术语—数据库中的知识发现或KDD的同义词。

而另一些人只是把数据挖掘视为数据库中知识发现过程的一个基本步骤。

知识发现的过程由以下步骤组成：1）数据清理：消除噪声或不一致数据，2）数据集成：多种数据可以组合在一起，3）数据选择：从数据库中检索与分析任务相关的数据，4）数据变换：数据变换或统一成适合挖掘的形式，如通过汇总或聚集操作，5）数据挖掘：基本步骤，使用智能方法提取数据模式，6）模式评估：根据某种兴趣度度量，识别表示知识的真正有趣的模式，7）知识表示：使用可视化和知识表示技术，向用户提供挖掘的知识。

数据挖掘的步骤可以与用户或知识库进行交互。

把有趣的模式提供给用户，或作为新的知识存放在知识库中。

注意，根据这种观点，数据挖掘只是整个过程中的一个步骤，尽管是最重要的一步，因为它发现隐藏的模式。

我们同意数据挖掘是知识发现过程中的一个步骤。

然而，在产业界、媒体和数据库研究界，“数据挖掘”比那个较长的术语“数据库中知识发现”更为流行。

因此，在本书中，选用的术语是数据挖掘。

我们采用数据挖掘的广义观点：数据挖掘是从存放在数据库中或其他信息库中的大量数据中挖掘出有趣知识的过程。

基于这种观点，典型的数据挖掘系统具有以下主要成分：数据库、数据仓库或其他信息库：这是一个或一组数据库、数据仓库、电子表格或其他类型的信息库。