Mike's Recommended Books on Data Mining

These are some of the books on data mining and statistics that I've found interesting or useful.

• Data Mining Techniques for Marketing, Sales, and Customer Support
  Michael J.A. Berry and Gordon Linoff, Wiley, 1997.
  

  Case studies and practical guidance. Good introductory text. A personal favorite.

  Other Reviews

• Data Mining: Concepts and Techniques
  Jiawei Han and Micheline Kamber, Morgan Kaufmann, 2001.
  

  Good algorithm descriptions. Covers the major areas in reasonable technical detail, with several alternative algorithms presented for classification, prediction, association rule induction and cluster analysis.

  Other Reviews

• Data Mining for Association Rules and Sequential Patterns
  Jean-Marc Adamo, Springer, 2001.
  

  Strictly a specialist book. The title says it all.

  Other Reviews

• Empirical Methods for Artificial Intelligence
  Paul Cohen, MIT Press, 1995.
  

  If you are working in artificial intelligence or data mining you are often in an unusual position: you are automatically generating one or more hypotheses based upon a sample of data, then testing the resulting hypothesis to see if it is true.

  Most statistics books adequately cover hypothesis testing. They cover the basic use of Null Hypothesis (is this hypothesis really needed), tests for Normal Distributions, etc. (Basic Business Statistics is definitely one of the better books if you need detailed coverage of these areas.)

  They do not, unfortunately, cover the material you really need for assessing the performance of programs which automatically generate their own hypothesis or interact in some way with their environment. This book does.

  Other Reviews

• Data Mining: Practical Machine Learning
  Ian H. Witten, Eibe Frank, Morgan Kaufmann, 2000.
  

  A practical and technical introduction to algorithms for data mining. Includes Java implementations of some of the major algorithms.

  Other Reviews

• Building Data Mining Applications for CRM
  Alex Berson, Stephen Smith, Kurt Thearling, McGraw-Hill, 2000.
  

  CRM (Customer Relationship Management) is a major application area for data mining. Some interesting chapters on the business applications and cost justifications. Good book if you are trying to figure out how data mining might fit into your business.

  Other Reviews

• Data Mining Explained: A Manager's Guide to Customer-Centric Business Intelligence
  Rhonda Delmater and Monte Hancock, Digital Press, 2001.
  

  Introduction to the methodology, techniques, and applications of data mining from a management perspective. The chapter on why data mining projects fail is well worth reading.

  Other Reviews

• Data Mining Your Website
  Jesus Mena, Digital Press, 1999.
  

  Aimed at executive (read non-specialist) level. Good introduction to some of the things you can do with web log data. The key message here is that it helps a lot if you know more about your visitors than just what pages they clicked on.

  Other Reviews

• Truth From Trash: How Learning Makes Sense
  Chris Thornton, MIT Press, 2000.
  

  Good introduction to machine learning, although I found the latter part of the book (from which it gets its title) a bit disappointing. The book's real strength is in placing existing machine learning methods in a good technical (and philosophical) perspective.

  Other Reviews

Note

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Michael Z. Bell

[Other Recommended Books]