current data mining applications in business intelligence

如有你有帮助，请购买下载，谢谢！数据挖掘Data Mining第一部 Data Mining的觀念............... 错误！未定义书签。

第一章何謂Data Mining ..................................................... 错误！未定义书签。

第二章Data Mining運用的理論與實際應用功能............. 错误！未定义书签。

第三章Data Mining與統計分析有何不同......................... 错误！未定义书签。

第四章完整的Data Mining有哪些步驟............................ 错误！未定义书签。

第五章CRISP-DM ............................................................... 错误！未定义书签。

第六章Data Mining、Data Warehousing、OLAP三者關係為何. 错误！未定义书签。

第七章Data Mining在CRM中扮演的角色為何.............. 错误！未定义书签。

第八章Data Mining 與Web Mining有何不同................. 错误！未定义书签。

第九章Data Mining 的功能................................................ 错误！未定义书签。

第十章Data Mining應用於各領域的情形......................... 错误！未定义书签。

第十一章Data Mining的分析工具..................................... 错误！未定义书签。

第二部多變量分析....................... 错误！未定义书签。

第一章主成分分析(Principal Component Analysis) ........... 错误！未定义书签。

数据挖掘导论英文版Data Mining IntroductionData mining is the process of extracting valuable insights and patterns from large datasets. It involves the application of various techniques and algorithms to uncover hidden relationships, trends, and anomalies that can be used to inform decision-making and drive business success. In today's data-driven world, the ability to effectively harness the power of data has become a critical competitive advantage for organizations across a wide range of industries.One of the key strengths of data mining is its versatility. It can be applied to a wide range of domains, from marketing and finance to healthcare and scientific research. In the marketing realm, for example, data mining can be used to analyze customer behavior, identify target segments, and develop personalized marketing strategies. In the financial sector, data mining can be leveraged to detect fraud, assess credit risk, and optimize investment portfolios.At the heart of data mining lies a diverse set of techniques and algorithms. These include supervised learning methods, such asregression and classification, which can be used to predict outcomes based on known patterns in the data. Unsupervised learning techniques, such as clustering and association rule mining, can be employed to uncover hidden structures and relationships within datasets. Additionally, advanced algorithms like neural networks and decision trees have proven to be highly effective in tackling complex, non-linear problems.The process of data mining typically involves several key steps, each of which plays a crucial role in extracting meaningful insights from the data. The first step is data preparation, which involves cleaning, transforming, and integrating the raw data into a format that can be effectively analyzed. This step is particularly important, as the quality and accuracy of the input data can significantly impact the reliability of the final results.Once the data is prepared, the next step is to select the appropriate data mining techniques and algorithms to apply. This requires a deep understanding of the problem at hand, as well as the strengths and limitations of the available tools. Depending on the specific goals of the analysis, the data mining practitioner may choose to employ a combination of techniques, each of which can provide unique insights and perspectives.The next phase is the actual data mining process, where the selectedalgorithms are applied to the prepared data. This can involve complex mathematical and statistical calculations, as well as the use of specialized software and computing resources. The results of this process may include the identification of patterns, trends, and relationships within the data, as well as the development of predictive models and other data-driven insights.Once the data mining process is complete, the final step is to interpret and communicate the findings. This involves translating the technical results into actionable insights that can be easily understood by stakeholders, such as business leaders, policymakers, or scientific researchers. Effective communication of data mining results is crucial, as it enables decision-makers to make informed choices and take appropriate actions based on the insights gained.One of the most exciting aspects of data mining is its continuous evolution and the emergence of new techniques and technologies. As the volume and complexity of data continue to grow, the need for more sophisticated and powerful data mining tools and algorithms has become increasingly pressing. Advances in areas such as machine learning, deep learning, and big data processing have opened up new frontiers in data mining, enabling practitioners to tackle increasingly complex problems and extract even more valuable insights from the data.In conclusion, data mining is a powerful and versatile tool that has the potential to transform the way we approach a wide range of challenges and opportunities. By leveraging the power of data and the latest analytical techniques, organizations can gain a deeper understanding of their operations, customers, and markets, and make more informed, data-driven decisions that drive sustainable growth and success. As the field of data mining continues to evolve, it is clear that it will play an increasingly crucial role in shaping the future of business, science, and society as a whole.。

铁塔公司CRM系统设计思路与方案金梦;何杰;王云【摘要】在集约高效、共建共享的思路下,中国铁塔公司建设了客户关系管理系统(CRM).介绍了中国铁塔CRM系统的建设背景、建设思路及建设方案,分析了全国集中建设的业务支撑系统所具备的优势及建设困难,并提出了下一步系统的演进方向.【期刊名称】《邮电设计技术》【年(卷),期】2016(000)012【总页数】5页(P25-29)【关键词】铁塔公司CRM;集中建设;业务支撑系统【作者】金梦;何杰;王云【作者单位】中国铁塔股份有限公司,北京100142;中国铁塔股份有限公司,北京100142;中国铁塔股份有限公司,北京100142【正文语种】中文【中图分类】TN915.5电信行业一直以来竞争激烈，为了保证网络覆盖优势，往往重复建设铁塔及相关基础设施，造成铁塔和土地资源的浪费。

铁塔公司在此背景下挂牌成立，不仅从根本上避免了我国电信运营商重复建设基础设施的问题，而且解决了目前共建共享存在的瓶颈。

这是我国深化国有企业改革，发展混合所有制经济，推动国有企业完善现代企业制度的有益探索。

中国铁塔作为一个新成立的公司，想要迅速取得企业的生存和发展空间，就需要建立一套完备的运营管理系统。

公司以集约、高效、安全为核心理念，全面构建集中式的运行管理和支持保障体系。

在铁塔集中建设运营方面，中国尚无先例，中国铁塔公司将建成全球规模最大的铁塔运营网络。

中国铁塔客户关系管理系统（CRM）在此基础上应运而生。

该系统基于互联网思维设计，结合铁塔独有的商务模式，打造了以订单为驱动，以客户为中心，覆盖铁塔资源售前、售中、售后全流程的销售服务体系。

本文介绍了中国铁塔CRM系统的建设背景、建设思路及建设方案，分析了全国集中建设的业务支撑系统所具备的优势及建设困难，并提供了下一步建设思路。

中国铁塔紧紧围绕“三步走”的战略安排，坚持高点定位、快速起步，全面创新、高效运营，以人为本、做强做优，全力打造体制好、机制好、服务好、成本低、竞争力强（“五好”）的企业形象，努力把中国铁塔建设成为集约化、规模化、专业化、高效化（“四化”）运营的国际一流的通信基础设施综合服务商。

TECHNOLOGY AND INFORMATION科学与信息化2023年1月下 187数据挖掘在公司财务分析中的应用探究刘哲1 张爽2 苗得庆11. 贵州食品工程职业学院 贵州 贵阳 551400；2. 贵州财经职业学院 贵州 贵阳 551400摘 要 在数字经济取得巨大发展的同时,各行各业都朝着信息化方向不断前进，企业的经营、管理理念也与时俱进。

企业在经营的过程中会产生大量的数据和信息，而在诸多数据和信息中，财务信息是反映企业经营真实状况的重要信息。

本文将结合数据挖掘技术的起源、发展历程及其特点，对数据挖掘在公司财务分析中的应用进行分析探讨，以供参考。

关键词 数据挖掘；财务分析；企业管理Exploration of Data Mining Application in Enterprise Financial Analysis Liu Zhe 1, Zhang Shuang 2, Miao De-qing 11. Guizhou V ocational College of Foodstuff Engineering, Guiyang 551400, Guizhou Province, China;2. Guizhou V ocational College of Finance and Economics, Guiyang 551400, Guizhou Province, ChinaAbstract While the digital economy has made great development, all walks of life are moving towards informatization, and the operation and management concepts of enterprises are also advancing with the times. Enterprises will produce a large amount of data and information in the process of operation, and among various data and information, financial information is an important information that reflects the real situation of the enterprise operation. This paper will analyze and discuss the application of data mining in enterprise financial analysis based on the origin, development process and characteristics of data mining technology for reference.Key words data mining; financial analysis; enterprise management1 数据挖掘定义数据挖掘，在20世纪90年代便有国外学者对其定义及重要性做出了相关阐释，比如1998年，WilliamE 等相继提出并且设计了3种关于数据挖掘的方法，该方法主要对交易往来的一些信息做出财务分析，为以后的学者对这一方面研究提供了大量的参考意义。

Data MiningCourse code：82133001Course name：Data miningCredits：3 term：10students：Undergraduates major in Statisticscourse requirement ：Probability，Mathematical StatisticsCourse director：Xu huanying，assistant，MasterCourse Description：《Data mining》is a professional elective course for the students in Statistics which mainly studies the basic concepts , methods , techniques and applications of data mining . It includes data preprocessing , the concept of summarization , the way for the number of decision-making , the prediction method of regression , and obtaining the Internet information by using the data mining methods , mining the Internet knowledge , and the data mining applications in network security . The purpose of this course is to enable students to master the methods and technologies of data mining and to know the applications of data mining in Internet information and intelligent information .Practical activity：No need.course assessmentFinal term grade =regular grade *30%+ Final exam grade*70%；regular grade is determined by Attendance situation and performance of homework ；Final exam will be conducted in the form of Closed book。

◎我所知道的一點Data Mining1.前言2.定義3.方法4.工具5.應用6.結論◎以上內容提供者:趙民德中央研究院統計科學研究所◎◎資料採礦（Data Mining）連載之一‧何謂DATA MINING‧DATA MINING和統計分析的不同‧為什麼需要DATA MINING何謂DATA MINING？資料採礦的工作（Data Mining）是近年來資料庫應用領域中，相當熱門的議題。

它是個神奇又時髦的技術，但卻也不是什麼新東西，因為Data Mining使用的分析方法，如預測模型（迴歸、時間數列）、資料庫分割（Database Segmentation）、連接分析（Link Analysis）、偏差偵測（Deviation Detection）等；美國政府從第二次世界大戰前，就在人口普查以及軍事方面使用這些技術，但是資訊科技的進展超乎想像，新工具的出現，例如關連式資料庫、物件導向資料庫、柔性計算理論（包括Neural network、Fuzzy theory、Genetic Algorithms、Rough Set等）、人工智慧的應用（如知識工程、專家系統），以及網路通訊技術的發展，使從資料堆中挖掘寶藏，常常能超越歸納範圍的關係；使Data Mining成為企業智慧的一部份。

Data Mining是一個浮現中的新領域。

在範圍和定義上、推理和期望上有一些不同。

挖掘的資訊和知識從巨大的資料庫而來，它被許多研究者在資料庫系統和機器學習（Machine learning）當作關鍵研究議題，而且也被企業體當作主要利基的重要所在。

有許多不同領域的專家，對Data Mining展現出極大興趣，例如在資訊服務業中，浮現一些應用，如在Internet之資料倉儲和線上服務，並且增加企業的許多生機。

隨著資訊科技的進步以及電子化時代的來臨，現今企業所面對的是一個與以往截然不同的競爭環境。

在資訊科技的推波助瀾下，不僅企業競爭的強度與速度倍數於以往，激增的市場交易也使得各企業所需儲存與處理的資料量越來越龐大。

1、Bilingual 双语Chinese English bilingual text 中英对照2、Data warehouse and Data Mining 数据仓库与数据挖掘3、classification 分类systematize classification 使分类系统化4、preprocess 预处理The theory and algorithms of automatic fingerprint identification system (AFIS) preprocess are systematically illustrated.摘要系统阐述了自动指纹识别系统预处理的理论、算法5、angle 角度6、organizations 组织central organizations 中央机关7、OLTP On-Line Transactional Processing 在线事物处理8、OLAP On-Line Analytical Processing 在线分析处理9、Incorporated 包含、包括、组成公司A corporation is an incorporated body 公司是一种组建的实体10、unique 唯一的、独特的unique technique 独特的手法11、Capabilities 功能Evaluate the capabilities of suppliers 评估供应商的能力12、features 特征13、complex 复杂的14、information consistency 信息整合15、incompatible 不兼容的16、inconsistent 不一致的Those two are temperamentally incompatible 他们两人脾气不对17、utility 利用marginal utility 边际效用18、Internal integration 内部整合19、summarizes 总结20、application-oritend 应用对象21、subject-oritend 面向主题的22、time-varient 随时间变化的23、tomb data 历史数据24、seldom 极少Advice is seldom welcome 忠言多逆耳25、previous 先前的the previous quarter 上一季26、implicit 含蓄implicit criticism 含蓄的批评27、data dredging 数据捕捞28、credit risk 信用风险29、Inventory forecasting 库存预测30、business intelligence（BI）商业智能31、cell 单元32、Data cure 数据立方体33、attribute 属性34、granular 粒状35、metadata 元数据36、independent 独立的37、prototype 原型38、overall 总体39、mature 成熟40、combination 组合41、feedback 反馈42、approach 态度43、scope 范围44、specific 特定的45、data mart 数据集市46、dependent 从属的47、motivate 刺激、激励Motivate and withstand higher working pressure个性积极，愿意承受压力.敢于克服困难48、extensive 广泛49、transaction 交易50、suit 诉讼suit pending 案件正在审理中51、isolate 孤立We decided to isolate the patients.我们决定隔离病人52、consolidation 合并So our Party really does need consolidation 所以，我们党确实存在一个整顿的问题53、throughput 吞吐量Design of a Web Site Throughput Analysis SystemWeb网站流量分析系统设计收藏指正54、Knowledge Discovery（KDD）55、non-trivial(有价值的）--Extraction interesting (non-trivial(有价值的), implicit（固有的）, previously unknown and potentially useful) patterns or knowledge from huge amounts of data.56、archeology 考古57、alternative 替代58、Statistics 统计、统计学population statistics 人口统计59、feature 特点A facial feature 面貌特征60、concise 简洁a remarkable concise report 一份非常简洁扼要的报告61、issue 发行issue price 发行价格62、heterogeneous (异类的)--Constructed by integrating multiple, heterogeneous (异类的)data sources63、multiple 多种Multiple attachments多实习64、consistent（一贯）、encode（编码）ensure consistency in naming conventions,encoding structures, attribute measures, etc.确保一致性在命名约定，编码结构，属性措施，等等。

Research Frontiers in Advanced Data MiningTechnologies and ApplicationsJiawei HanDepartment of Computer Science,University of Illinois at Urbana-ChampaignAbstract.Research in data mining has two general directions:theoretical foun-dations and advanced technologies and applications.In this talk,we will focuson the research issues for advanced technologies and applications in data miningand discuss some recent progress in this direction,including(1)pattern min-ing,usage,and understanding,(2)information network analysis,(3)stream datamining,(4)mining moving object data,RFID data,and data from sensor net-works,(5)spatiotemporal and multimedia data mining,(6)biological datamining,(7)text and Web mining,(8)data mining for software engineering andcomputer system analysis,and(9)data cube-oriented multidimensional onlineanalytical processing.Data mining,as the confluence of multiple intertwined disciplines,including statis-tics,machine learning,pattern recognition,database systems,information retrieval, World-Wide Web,and many application domains,has achieved great progress in the past decade[1].Similar to many researchfields,data mining has two general direc-tions:theoretical foundations and advanced technologies and applications.Here we fo-cus on advanced technologies and applications in data mining and discuss some recent progress in this direction.Notice that some popular research topics,such as privacy-preserving data mining,are not covered in the discussion for lack of space/time.Our discussion is organized into nine themes,and we briefly outline the current status and research problems in each theme.1Pattern Mining,Pattern Usage,and Pattern Understanding Frequent pattern mining has been a focused theme in data mining research for over a decade.Abundant literature has been dedicated to this research and tremendous progress has been made,ranging from efficient and scalable algorithms for frequent itemset min-ing in transaction databases to numerous research frontiers,such as sequential pattern mining,structural pattern mining,correlation mining,associative classification,and frequent-pattern-based clustering,as well as their broad applications.Recently,studies have proceeded to scalable methods for mining colossal patterns where the size of the patterns could be rather large so that the step-by-step growth using an Apriori-like approach does not work,methods for pattern compression,extraction of high-quality top-k patterns,and understanding patterns by context analysis and gener-ation of semantic annotations.Moreover,frequent patterns have been used for effective Z.-H.Zhou,H.Li,and Q.Yang(Eds.):PAKDD2007,LNAI4426,pp.1–5,2007.c Springer-Verlag Berlin Heidelberg20072J.Hanclassification by top-k rule generation for long patterns and discriminative frequent pat-tern analysis.Frequent patterns have also been used for clustering of high-dimensional biological data.Scalable methods for mining long,approximate,compressed,and so-phisticated patterns for advanced applications,such as biological sequences and net-works,and the exploration of mined patterns for classification,clustering,correlation analysis,and pattern understanding will still be interesting topics in research.2Information Network AnalysisGoogle’s PageRank algorithm has started a revolution on Internet search.However, since information network analysis covers many additional aspects and needs scalable and effective methods,the systematic study of this domain has just started,with many interesting issues to be rmation network analysis has broad applications, covering social and biological network analysis,computer network intrusion detection, software program analysis,terrorist network discovery,and Web analysis.One interesting direction is to treat information network as graphs and further de-velop graph mining methods.Recent progress on graph mining and its associated struc-tural pattern-based classification and clustering,graph indexing,and similarity search will play an important role in information network analysis.Moreover,since informa-tion networks often form huge,multidimensional heterogeneous graphs,mining noisy, approximate,and heterogeneous subgraphs based on different applications for the con-struction of application-specific networks with sophisticated structures will help in-formation network analysis substantially.The discovery of the power law distribu-tion of information networks and the rules on density evolution of information net-works will help develop effective algorithms for network analysis.Finally,the study of link analysis,heterogeneous data integration,user-guided clustering,user-based net-work construction,will provide essential methodology for the in-depth study in this direction.3Stream Data MiningStream data refers to the data thatflows into the system in vast volume,changing dy-namically,possibly infinite,and containing multi-dimensional features.Such data can-not be stored in traditional database systems,and moreover,most systems may only be able to read the stream once in sequential order.This poses great challenges on effective mining of stream data.With substantial research,progress has bee made on efficient methods for mining fre-quent patterns in data streams,multidimensional analysis of stream data(such as con-struction of stream cubes),stream data classification,stream clustering,stream outlier analysis,rare event detection,and so on.The general philosophy is to develop single-scan algorithms to collective information about stream data in tilted time windows, exploring micro-clustering,limited aggregation,and approximation.It is important toResearch Frontiers in Advanced Data Mining Technologies and Applications3 explore new applications of stream data mining,e.g.,real-time detection of anomaly in computer networks,power-gridflow,and other stream data.4Mining Moving Object Data,RFID Data,and Data from Sensor NetworksWith the popularity of sensor networks,GPS,cellular phones,other mobile devices, and RFID technology,tremendous amount of moving object data has been collected, calling for effective analysis.There are many new research issues on mining moving object data,RFID data,and data from sensor networks.For example,how to explore correlation and regularity to clean noisy sensor network and RFID data,how to integrate and construct data warehouses for such data,how to perform scalable mining for peta-byte RFID data,how tofind strange moving objects,how to cluster trajectory data, and so on.With time,location,moving direction,speed,as well as multidimensional semantics of moving object data,likely multi-dimensional data mining will play an essential role in this study.5Spatiotemporal and Multimedia Data MiningThe real world data is usually related to space,time,and in multimedia modes(e.g., containing color,image,audio,and video).With the popularity of digital photos,audio DVDs,videos,YouTube,Internet-based map services,weather services,satellite im-ages,digital earth,and many other forms of multimedia and spatiotemporal data,min-ing spatial,temporal,spatiotemporal,and multimedia data will become increasingly popular,with far-reaching implications.For example,mining satellite images may help detect forestfire,find unusual phenomena on earth,and predict hurricanes,weather patterns,and global warming trends.Research in this domain needs the confluence of multiple disciplines including im-age processing,pattern recognition,parallel processing,and data mining.Automatic categorization of images and videos,classification of spatiotemporal data,finding fre-quent/sequential patterns and outliers,spatial collocation analysis,and many other tasks have been studied popularly.With the mounting in many applications,scalable analysis of spatiotemporal and multimedia data will be an important research frontier for a long time.6Biological Data MiningWith the fast progress of biological research and the accumulation of vast amount of biological data(especially,a great deal of it has been made available on the Web),bi-ological data mining has become a very activefield,including comparative genomics, evolution and phylogeny,biological databases and data integration,biological sequence analysis,biological network analysis,biological image analysis,biological literature analysis(e.g.,PubMed),and systems biology.This domain is largely overlapped with4J.Hanbioinformatics but data mining researchers has been emphasizing on integrating biolog-ical databases with biological data integration,constructing biological data warehouses, analyzing biological networks,and developing various kinds of scalable bio-data min-ing algorithms.Advances in biology,medicine,and bioinformatics provide data miners with abun-dant real data sets and a broad spectrum of challenging research problems.It is expected that an increasing number of data miners will devoted themselves to this domain and make contributions to the advances in both bioinformatics and data mining.7Text and Web MiningThe Web has become the ultimate information access and processing platform,housing not only billions of link-accessed“pages”,containing textual data,multimedia data,and linkages,on the surface Web,but also query-accessed“databases”on the deep Web. With the advent of Web2.0,there is an increasing amount of dynamic“workflow”emerging.With its penetrating deeply into our daily life and evolving into unlimited dynamic applications,the Web is central in our information infrastructure.Its virtually unlimited scope and scale render immense opportunities for data mining.Text mining and information extraction have been applied not only to Web mining but also to the analysis of other kinds of semi-structured and unstructured informa-tion,such as digital libraries,biological information systems,business intelligence and customer relationship management,computer-aided instructions,and office automation systems.There are lots of research issues in this domain,which takes the collaborative efforts of multiple disciplines,including information retrieval,databases,data mining,natural language processing,and machine learning.Some promising research topics include heterogeneous information integration,information extraction,personalized informa-tion agents,application-specific partial Web construction and mining,in-depth Web semantics analysis,and turning Web into relatively structured information-base.8Data Mining for Software Engineering and Computer System AnalysisSoftware program executions and computer system/network operations potentially gen-erate huge amounts of data.Data mining can be performed on such data to monitor system status,improve system performance,isolate software bugs,detect software pla-giarism,analyze computer system faults,uncover network intrusions,and recognize system malfunctions.Data mining for software and system engineering can be partitioned into static anal-ysis and dynamic/stream analysis,based on whether the system can collect traces be-forehand for post-analysis or it must react at real time to handle online data.Differ-ent methods have been developed in this domain by integration and extension of the methods developed in machine learning,data mining,pattern recognition,and statis-tics.However,this is still a rich domain for data miners with further development of sophisticated,scalable,and real-time data mining methods.Research Frontiers in Advanced Data Mining Technologies and Applications5 9Data Cube-Oriented Multidimensional Online Analytical ProcessingViewing and mining data in multidimensional space will substantially increase the power andflexibility of data analysis.Data cube computation and OLAP(online analytical processing)technologies developed in data warehouse have substantially in-creased the power of multidimensional analysis of large datasets.Besides traditional data cubes,there are recent studies on construction of regression cubes,prediction cubes,and other sophisticated statistics-oriented data cubes.Such multi-dimensional, especially high-dimensional,analysis tools will ensure data can be analyzed in hier-archical,multidimensional structures efficiently andflexibly at user’sfinger tips.This leads to the integration of online analytical processing with data mining,called OLAP mining.We believe that OLAP mining will substantially enhance the power andflexibility of data analysis and bring the analysis methods derived from the research in machine learning,pattern recognition,and statistics into convenient analysis of massive data with hierarchical structures in multidimensional space.It is a promising researchfield that may lead to the popular adoption of data mining in information industry. Reference1.Han,J.,Kamber,M.:Data Mining:Concepts and Techniques(2nd ed.).Morgan Kaufmann(2006)。

致 谢值此论文完成之际，作者首先要衷心感谢导师邵良杉教授的悉心指导和淳淳教诲。

邵老师严谨治学、开拓创新的学术作风，谦虚豁达、平易近人的高尚人格，勤勉踏实、兢兢业业的工作态度，对我在做人、治学、工作和生活等方面产生了极大影响，将使我终身受益。

在此，谨向导师致以崇高的敬意和真诚的感谢!感谢一起学习和生活的各位同学。

学术上的交流促进了我们彼此的科研，生活中大家一起分享阳光，分担风雨，一起面对学习中的压力与挑战，一起度过了愉快而短暂的美好时光。

这些同学包括已经毕业的师兄师姐，一同入校的同学，宿舍里同住的姐妹以及实验室里一起学习的师弟师妹们。

难得的友情我一定会铭记终生。

最后感谢父母，生活上的关怀和精神上的理解与鼓励，使我能够面对各种困难与挫折，让我充满信心和勇气。

他们的默默支持，是促使我完成学业的最大动力。

最后我要将本文献给所有支持和帮助过我的人，向他们表达我最诚挚的谢意。

摘 要随着信息技术和数据库技术的高速发展，人们每天都要面对巨大的数据量，数据挖掘正是致力于数据的分析和理解、揭示数据内部蕴藏知识的技术，是当前人工智能研究中非常活跃的领域。

粗糙集理论是一种有效地处理模糊性和不确定性问题的数学工具，为数据挖掘的研究提供了新的思路和基础。

本文主要研究变精度粗糙集的约简算法，针对传统数据挖掘处理噪声数据不力的问题，从理论和应用两个方面对约简算法进行了深入的研究。

主要工作包括:(1) 在变精度粗糙集理论下对经典粗糙集的概念进行了重新的诠释;分析了粗糙集理论在数据挖掘应用中的理论根据和基本原理，并点出了研究的方向。

(2)比较分析了两种变精度粗糙集模型下的约简算法，即−β下近似和−β下分布约简算法，结合这两种算法提出了一种改进算法，并验证了新算法的有效性。

(3) 提出了基于变精度粗糙集和熵权相结合的评估模型，并将模型应用于企业自主创新能力评价中，通过实证分析，证实了该模型在企业自主创新能力评价中的有效性。

关键词：变精度粗糙集；属性约简；熵权；自主创新能力AbstractAs information technology and database technology developing rapidly, people every day face the enormous amount of data,Data mining is a technology that dedicated to data analysis and understanding, revealing hidden knowledge of the internal data ,and is currently a very active area of research of AI. Rough set theory is an effective way of dealing with ambiguity and uncertainty of the mathematical tools for data mining research has provided new ideas and the foundation.This paper studies the variable precision rough set reduction algorithm ,for traditional data mining deal with the noise problem of insufficient data, from both theoretical and applied aspects of reduction algorithm in-depth study.Main functions include:1) Re-interpret the concept of the classic rough set based on the variable precision rough set theory; analysis of rough set theory in data mining applications, the theoretical basis and rationale, and point out research directions.2) A comparative analysis of two kinds of variable precision rough set model of the reduction algorithm, namely, the βlower approximation andβlower distribution reduction algorithm, combining the two algorithms proposed an improved algorithm and verify that the new algorithm.3) Propose an assessment model based on variable precision rough set and entropy, and the model was applied to evaluation of enterprise independent innovation capacity, through empirical analysis confirms the model capability of independent innovation in the enterprise evaluation of effectiveness.Key Words：Variable precision rough set； attribute reduction ；entropy；capability of independent innovation目 录摘要Abstract1 引言 (1)1.1 论文研究背景及意义 (1)1.2 国内外研究综述 (2)1.2.1粗糙集理论的发展及研究现状 (2)1.2.2 数据挖掘方法的研究现状 (7)1.3 论文主要研究内容和结构安排 (8)2 相关理论概述 (10)粗糙集基本理论2.1 (10)2.2变精度粗糙集理论 (15)2.3变精度粗糙集理论和其他挖掘算法的结合应用 (17)3 基于变精度粗糙集的属性约简算法 (19)β近似属性约简算法 (19)3.1 变精度粗糙集中的−β下分布属性约简算法 (22)3.2 变精度粗糙集下的−β下分布约简的基本思想 (22)3.2.1 −β下分布可辨识矩阵 (23)3.2.2 −β下近似属性约简算法 (25)3.3 改进的VPRS下的−3.4实验结果及分析 (29)4 基于VPRS-熵权法的企业自主创新能力评价研究 (31)4.1 自主创新理论阐述 (31)4.1.1 创新概念的提出 (31)4.1.2 自主创新的内涵 (32)4.1.3 企业创新能力及测度理论 (32)4.2信息熵与熵权 (35)4.2.1信息熵 (36)4.2.2 熵权 (36)4.3 熵值法计算步骤 (38)4.4基于VPRS下近似约简算法的建模过程 (39)4.5 实证分析 (40)4.5.1 初选评价指标及待评对象确定 (40)4.5.2 原始数据采集及数据预处理 (41)4.5.3 指标约简 (45)4.5.4 确定熵值、权重和综合评价 (46)5 结论 (49)5.1研究工作总结 (49)5.2展望 (50)致谢 (50)参考文献 (51)作者简历 (54)学位论文原创性声明 (55)学位论文数据集 (56)1 引言1.1 论文研究背景及意义20世纪90年代以来，随着科技的进步，特别是信息产业的发展和普及，把我们带入了一个崭新的信息时代。

Part II Data MiningOutlineThe Concept of Data Mining(数据挖掘概念) Architecture of a Typical Data Mining System (数据挖掘系统结构)What can be Mined? (能挖掘什么？)Major Issues(主要问题)in Data MiningData Cleaning(数据清理)3What Is Data Mining?Data mining is the process of discovering interesting knowledge from large amounts of data. (数据挖掘是从大量数据中发现有趣知识的过程) The main difference that separates information retrieval apart from data mining is their goals. (数据挖掘和信息检索的主要差别在于他们的目标) Information retrieval is to help users search for documents or data that satisfy their information needs(信息检索帮用户寻找他们需要的文档/数据)e.g. Find customers who have purchased more than $10,000 in the last month .(查找上个月购物量超过1万美元的客户)Data mining discovers useful knowledge by analyzing data correlations using sophisticated data mining techniques(数据挖掘用复杂技术分析…)e.g. Find all items which are frequently purchased with milk .(查找经常和牛奶被购买的商品)A KDD Process (1) Some people view data mining as synonymous5A KDD Process (2)Learning the application domain (学习应用领域相关知识):Relevant knowledge & goals of application (相关知识和目标) Creating a target data set (建立目标数据集) Data selection, Data cleaning and preprocessing (预处理)Choosing functions of data mining (选择数据挖掘功能)Summarization, classification, association, clustering , etc.Choosing the mining algorithm(s) (选择挖掘算法)Data mining (进行数据挖掘): search for patterns of interest Pattern evaluation and knowledge presentation (模式评估和知识表示)Removing redundant patterns, visualization, transformation, etc.Present results to user in meaningful manner.Use of discovered knowledge (使用所发现的知识)7Concept/class description (概念/类描述)Characterization(特征): provide a summarization of the given data set Comparison(区分): mine distinguishing characteristics(挖掘区别特征)that differentiate a target class from comparable contrasting classes. Association rules (correlation and causality)(关联规则)Association rules are of the form(这种形式的规则): X ⇒Y,Examples: contains(T, “computer”) ⇒contains(T, “software”)[support = 1%, confidence = 50%]age(X, “20..29”) ∧income(X, “20..29K ”) ⇒buys(X, “PC ”)[support = 2%, confidence = 60%]Classification and Prediction (分类和预测)Find models that describe and distinguish classes for future prediction.What kinds of patterns can be mined?(1)What kinds of patterns can be mined?(2)Cluster(聚类)Group data to form some classes(将数据聚合成一些类)Principle: maximizing the intra-class similarity and minimizing the interclass similarity (原则: 最大化类内相似度，最小化类间相似度) Outlier analysis: objects that do not comply with the general behavior / data model. (局外者分析: 发现与一般行为或数据模型不一致的对象) Trend and evolution analysis (趋势和演变分析)Sequential pattern mining(序列模式挖掘)Regression analysis(回归分析)Periodicity analysis(周期分析)Similarity-based analysis(基于相似度分析)What kinds of patterns can be mined?(3)In the context of text and Web mining, the knowledge also includes: (在文本挖掘或web挖掘中还可以发现)Word association (术语关联)Web resource discovery (WEB资源发现)News Event (新闻事件)Browsing behavior (浏览行为)Online communities (网上社团)Mining Web link structures to identify authoritative Web pages finding spam sites (发现垃圾网站)Opinion Mining (观点挖掘)…10Major Issues in Data Mining (1)Mining methodology(挖掘方法)and user interactionMining different kinds of knowledge in DBs (从DB 挖掘不同类型知识) Interactive mining of knowledge at multiple levels of abstraction (在多个抽象层上交互挖掘知识)Incorporation of background knowledge (结合背景知识)Data mining query languages (数据挖掘查询语言)Presentation and visualization of data mining results(结果可视化表示) Handling noise and incomplete data (处理噪音和不完全数据) Pattern evaluation (模式评估)Performance and scalability (性能和可伸缩性) Efficiency(有效性)and scalability(可伸缩性)of data mining algorithmsParallel(并行), distributed(分布) & incremental(增量)mining methods©Wu Yangyang 11Major Issues in Data Mining (2)Issues relating to the diversity of data types (数据多样性相关问题)Handling relational and complex types of data (关系和复杂类型数据) Mining information from heterogeneous databases and www(异质异构) Issues related to applications (应用相关的问题) Application of discovered knowledge (所发现知识的应用)Domain-specific data mining tools (面向特定领域的挖掘工具)Intelligent query answering (智能问答) Process control(过程控制)and decision making(决策制定)Integration of the discovered knowledge with existing knowledge:A knowledge fusion problem (知识融合)Protection of data security(数据安全), integrity(完整性), and privacy12CulturesDatabases: concentrate on large-scale (non-main-memory) data.(数据库：关注大规模数据)To a database person, data-mining is an extreme form of analytic processing. Result is the data that answers the query.(对数据库工作者而言数据挖掘是一种分析处理, 其结果就是问题答案) AI (machine-learning): concentrate on complex methods, small data.(人工智能(机器学习)：关注复杂方法，小数据)Statistics: concentrate on models. (统计：关注模型.)To a statistician, data-mining is the inference of models. Result is the parameters of the model (数据挖掘是模型推论, 其结果是一些模型参数)e.g. Given a billion numbers, a statistician might fit the billion points to the best Gaussian distribution and report the mean and standard deviation.©Wu Yangyang 13Data Cleaning (1)Data Preprocessing (数据预处理):Cleaning, integration, transformation, reduction, discretization (离散化) Why data cleaning? (为什么要清理数据？)--No quality data, no quality mining results! Garbage in, Garbage out! Measure of data quality (数据质量的度量标准)Accuracy (正确性)Completeness (完整性)Consistency(一致)Timeliness(适时)Believability(可信)Interpretability(可解释性) Accessibility(可存取性)14Data Cleaning (2)Data in the real world is dirtyIncomplete (不完全)：Lacking some attribute values (缺少一些属性值)Lacking certain interest attributes /containing only aggregate data(缺少某些有用属性或只包含聚集数据)Noisy(有噪音): containing errors or outliers(包含错误或异常) Inconsistent: containing discrepancies in codes or names(不一致: 编码或名称存在差异)Major tasks in data cleaning (数据清理的主要任务)Fill in missing values (补上缺少的值)Identify outliers(识别出异常值)and smooth out noisy data(消除噪音)Correct inconsistent data(校正不一致数据) Resolve redundancy caused by data integration (消除集成产生的冗余)15Data Cleaning (3)Handle missing values (处理缺值问题) Ignore the tuple (忽略该元组) Fill in the missing value manually (人工填补) Use a global constant to fill in the missing value (用全局常量填补) Use the attribute mean to fill in the missing value (该属性平均值填补) Use the attribute mean for all samples belonging to the same class to fill in the missing value (用同类的属性平均值填补) Use the most probable value(最大可能的值)to fill in the missing value Identify outliers and smooth out noisy data(识别异常值和消除噪音)Binning method (分箱方法):First sort data and partition into bins (先排序、分箱)Then one can smooth by bin means, smooth by bin median, smooth by bin boundaries, etc.(然后用平均值、中值、边界值平滑)©Wu Yangyang 16Data Cleaning (4)Example: Sorted data: 4, 8, 9, 15, 21, 21, 24, 25, 26, 28, 29, 34 Partition into (equi-depth) bins (分成等深的箱):-Bin 1: 4, 8, 9, 15-Bin 2: 21, 21, 24, 25-Bin 3: 26, 28, 29, 34Smoothing by bin means (用平均值平滑):-Bin 1: 9, 9, 9, 9-Bin 2: 23, 23, 23, 23-Bin 3: 29, 29, 29, 29Smoothing by bin boundaries (用边界值平滑):-Bin 1: 4, 4, 4, 15-Bin 2: 21, 21, 25, 25-Bin 3: 26, 26, 26, 34Clustering (。