Data Mining Techniques 3rd edition

Introduction.

1. What Is Data Mining and Why Do It?
2. Data Mining Applications in Marketing and Customer Relationship Management.
3. The Data Mining Process.
4. Statistics 101: What You Should Know About Data.
5. Descriptions and Prediction: Profiling and Predictive Modeling.
6. Data Mining Using Classic Statistical Techniques.
7. Decision Trees.
8. Artificial Neural Networks.
9. Nearest Neighbor Approaches: Memory-Based Reasoning and Collaborative Filtering.
10. Knowing When to Worry: Using Survival Analysis to Understand Customers.
11. Genetic Algorithms and Swarm Intelligence.
12. Tell Me Something New: Pattern Discovery and Data Mining.
13. Finding Islands of Similarity: Automatic Cluster Detection.
14. Alternative Approaches to Cluster Detection.
15. Market Basket Analysis and Association Rules.
16. Link Analysis.
17. Data Warehousing, OLAP, Analytic Sandboxes, and Data Mining.
18. Building Customer Signatures.
19. Derived Variables: Making the Data Mean More.
20. Too Much of a Good Thing? Techniques for Reducing the Number of Variables.
21. Listen Carefully to What Your Customers Say: Text Mining.

Index.