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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