Recommended Books on Data Mining
These are some of the books on data mining and statistics that we've found
interesting or useful.
Data Mining: Concepts and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems)
Jiawei Han, Micheline Kamber, Jian Pei, Morgan Kaufmann , 2011.
Good algorithm descriptions. Covers the major areas in reasonable
technical detail, with several alternative algorithms presented
for classification, prediction, association rule induction and cluster
Data Mining Techniques: For Marketing, Sales, and Customer Support
Michael J. A. Berry, Gordon S. Linoff, Wiley
Case studies and practical guidance. Good introductory text. A personal favorite.
Data Mining Your Website
Jesus Mena, Digital Press
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.
Data Mining Explained: A Manager's Guide to Customer-Centric Business Intelligence
Rhonda Delmater, Monte Hancock Jr., Digital Press
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.
Data Mining for Association Rules and Sequential Patterns: Sequential and Parallel Algorithms
Jean-Marc Adamo, Springer
Strictly a specialist book. The title says it all.
Building Data Mining Applications for CRM
Alex Berson, Stephen J. Smith, Berson, Kurt Thearling, McGraw-Hill Companies
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.
Truth from Trash: How Learning Makes Sense (Complex Adaptive Systems)
Christopher J. Thornton, A Bradford Book
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.
Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (The Morgan Kaufmann Series in Data Management Systems)
Ian H. Witten, Eibe Frank, Morgan Kaufmann
A practical and technical introduction to algorithms for data mining. Includes
Java implementations of some of the major algorithms.
Empirical Methods for Artificial Intelligence (MIT Press)
Paul R. Cohen, A Bradford Book
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 Recommended Books]