Friday, April 1, 2011

Data Mining Techniques 3rd Edition

Gordon and I spent much of the last year writing the third edition of Data Mining Techniques and now, at last, I am holding the finished product in my hand. In the 14 years since the first edition came out, our knowledge has increased by a factor of at least 10 while the page count has only doubled so I estimate the information density has increased by a factor of five! I hope reviewers will agree that our writing skills have also improved with time and practice. In short, I'm very proud of our latest effort and I hope our readers will continue to find it useful for the next 14 years!

Table of Contents
Chapter 1 What Is Data Mining and Why Do It? 1
Chapter 2 Data Mining Applications in Marketing and Customer Relationship Management 27
Chapter 3 The Data Mining Process 67
Chapter 4 Statistics 101: What You Should Know About Data 101
Chapter 5 Descriptions and Prediction: Profi ling and Predictive Modeling 151
Chapter 6 Data Mining Using Classic Statistical Techniques 195
Chapter 7 Decision Trees 237
Chapter 8 Artifi cial Neural Networks 283
Chapter 9 Nearest Neighbor Approaches: Memory-Based Reasoning and Collaborative Filtering 323
Chapter 10 Knowing When to Worry: Using Survival Analysis to Understand Customers 359
Chapter 11 Genetic Algorithms and Swarm Intelligence 399
Chapter 12 Tell Me Something New: Pattern Discovery and Data Mining 431
Chapter 13 Finding Islands of Similarity: Automatic Cluster Detection 461
Chapter 14 Alternative Approaches to Cluster Detection 501
Chapter 15 Market Basket Analysis and Association Rules 537
Chapter 16 Link Analysis 583
Chapter 17 Data Warehousing, OLAP, Analytic Sandboxes, and Data Mining 615
Chapter 18 Building Customer Signatures 657
Chapter 19 Derived Variables: Making the Data Mean More 695
Chapter 20 Too Much of a Good Thing? Techniques for Reducing the Number of Variables 737
Chapter 21 Listen Carefully to What Your Customers Say: Text Mining 777
Index 823