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 Exercises Chapter 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 Exercises Chapter 3 Basic Data Mining Techniques Chapter 4 An Excel-Based Data Mining Tool Part 2 Advanced Data Mining Techniques Chapter 8 Nerual Networks Chapter 9 Building Nerual Networks with IDA Chapter 10 Staticstical Techniques Chapter 11 Specialized Techniques Part 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
