Data Mining : Practical Machine Learning Tools and Techniques, Second Edition
The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more.
This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses.
- Paperback | 560 pages
- 190.5 x 233.68 x 30.48mm | 1,065.94g
- 13 Jul 2005
- ELSEVIER SCIENCE & TECHNOLOGY
- Morgan Kaufmann Publishers In
- San Francisco, United States
- 2nd edition
Other books in this series
01 Aug 2017
25 Apr 2008
16 Mar 2012
16 Dec 2014
14 Mar 2008
19 May 2005
24 Jul 2009
Mixed media product
09 Oct 2006
04 Jun 2001
01 Jun 2011
12 Aug 2014
01 Dec 2009
Table of contents
1. What's it all about?
2. Input: Concepts, instances, attributes
3. Output: Knowledge representation
4. Algorithms: The basic methods
5. Credibility: Evaluating what's been learned
6. Implementations: Real machine learning schemes
7. Transformations: Engineering the input and output
8. Moving on: Extensions and applications
Part II: The Weka machine learning workbench
9. Introduction to Weka
10. The Explorer
11. The Knowledge Flow interface
12. The Experimenter
13. The command-line interface
14. Embedded machine learning
15. Writing new learning schemes
About Ian H. Witten
- Peter Norvig, Director of Search Quality, Google, Inc.
"This book presents this new discipline in a very accessible form: both as a text to train the next generation of practitioners and researchers, and to inform lifelong learners like myself. Witten and Frank have a passion for simple and elegant solutions. They approach each topic with this mindset, grounding all concepts in concrete examples, and urging the reader to consider the simple techniques first, and then progress to the more sophisticated ones if the simple ones prove inadequate. If you have data that you want to analyze and understand, this book and the associated Weka toolkit are an excellent way to start."
- From the foreword by Jim Gray, Microsoft Research
"It covers cutting-edge, data mining technology that forward-looking organizations use to successfully tackle problems that are complex, highly dimensional, chaotic, non-stationary (changing over time), or plagued by. The writing style is well-rounded and engaging without subjectivity, hyperbole, or ambiguity. I consider this book a classic already!"
- Dr. Tilmann Bruckhaus, StickyMinds.com