数据挖掘基础教程(附光盘·影印版) PDF下载

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