数据挖掘基础教程

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

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

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。