Berry's name appears first on the 2nd ed.

Includes index.

What is data mining and why do it? -- Data mining applications in marketing the customer relationship management -- The data mining process -- Statistics 101: What you should know about data -- Descriptions and prediction: Profiling and predictive modeling -- Data mining using classic statistical techniques -- Decision trees -- Artificial neural networks -- Nearest neighbor approaches: Memory-based reasoning and collaborative filtering -- Knowing when to worry: Using survival analysis to understand customers -- Genetic algorithms and swarm intelligence -- Tell me something new: Pattern discovery and data mining -- Finding islands of similarity: Automatic cluster detection -- Alternative approaches to cluster detection -- Market basket analysis and association rules -- Link analysis -- Data warehousing, OLAP, analytic sandboxes, and data mining -- Building customer signatures -- Derived variables: Making data mean more -- Too much of a good thing? Techniques for reducing the number of variables -- Listen carefully to what your customers say: Text mining.