数据挖掘基础教程

出版时间：2003-12  出版社：清华大学出版社  作者：罗伊尔  页数：350  
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内容概要

数据挖掘就是发现数据模型，以助于解释当前行为或预测将来的可能结果。本书介绍了数据挖掘的基本过程，解释了如何将数据挖掘应用于解决实际问题，从而使你能将数据挖掘技术应用于自己的实际工作中去。本书讲述了数据挖掘和知识发现的各方面内容，并着重介绍了数据挖掘模型的建立与测试，以及数据挖掘结果的解释与验证等内容。为了使读者更好地理解数据挖掘过程，在本书配套光盘中提供了一个基于Microsoft Excel的数据挖掘工具，读者可以亲身体验数据挖掘模型的建立与测试。 
　　本书可作为相关专业的本科生教材，对需要理解数据挖掘和智能系统的专业人员也是很好的参考书。

书籍目录

Part I Data Mining Fundamentals chapter 1 Data Mining:A First View      1.1 Data Mining:A Definition     1.2 What Can Computers Learn?         Three concept Views         Supervised Learing          Supervised Learing:A Decision for Tree Example          Unsupervised Clustering     1.3 Is Data Mining Appropriate for My Problem?         Data Mining or Data Query?         Data Mining vs.Data Query:An Example      1.4 Expert Systems or Data Mining?     1.5 A Simple Data Mining Process Model         Assembling the Data         The Data Warehouse          Relational Databases and Flat Files         Mining the Data         Interpreting the Results         Result application     1.6 Why Not Simple Search?     1.7 Data Mining Applications         Example Applications         Customer Intrinsic Value      1.8 chapter Summary     1.9 Key Terms     1.10 ExercisesChapter 2 Data Mining:A closer Look     2.1 Data Mining Strategies         classification         Estimation         Prediction         Unsupervised clustering          Market Basket Ananlysis     2.2 Supervised Data Mining Database         the Credit Card Promotion Database          Production Rules         Neural Networks         Statistical Regression     2.3 Association Rules     2.4 Clustering techniques     2.5 Evaluating Performance         evaluating supervised Learner Models          Two Class Error Analysis         Evaluating Numeric Output          Unsupervised Moedl Evaluation     2.6 chapter Summary      2.7 Key Terms     2.8 ExercisesChapter 3 Basic Data Mining TechniquesChapter 4 An Excel-Based Data Mining ToolPart 2 Advanced Data Mining Techniques  Chapter 8 Nerual Networks  Chapter 9 Building Nerual Networks with IDA  Chapter 10 Staticstical Techniques  Chapter 11 Specialized TechniquesPart 4:Intelligent Systems  Chapter 12 Rule-Based Systems  Chapter 13 Managing Uncertainty in Rule-Based System  Chapter 14 Intelligent Agents  Appendixes  Appendix A The iDASoftware  Appendix B Datasets for Data Mining   Appendix C Decision Tree Atrribute Selection  Appendix D Statistics for Performance Evaluation  Appendix E Excel Pivot Tables:Office 97  Bibliography  Index

用户评论 (总计4条)

•   有完备的数据集供练习使用,也有详细的实例,浅显易懂,适合入门学习.国内有翻译的中文版,但感觉还是英文版的读着舒服.
•   总体来说，书的结构不错，可以作为入门读物来看，内容较为简单，根据个人要求而定。如果你对datamining有一定的基础，可以不考虑这本书。
•   因为我们上课要用，所以就买了感觉还不错吧对提高英语阅读能力挺好的
•   本书缺乏逻辑，条理；赘述很多；不少章节文不对题。