Details

No. of pages:
664
Language:
English
Copyright:
© 2011
Published:
Imprint:
Morgan Kaufmann
Print ISBN:
9780123748560
Electronic ISBN:
9780080890364

About the authors

Ian Witten

Affiliations and Expertise

Professor, Computer Science Department, University of Waikato, New Zealand.

Eibe Frank

Affiliations and Expertise

Associate Professor, Department of Computer Science, University of Waikato, Hamilton, New Zealand

Mark Hall

Affiliations and Expertise

Honorary Research Associate, Computer Science Department, University of Waikato, New Zealand

Reviews

"...offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations."

"Co-author Witten is the author of other well-known books on data mining, and he and his co-authors of this book excel in statistics, computer science, and mathematics. Their in- depth backgrounds and insights are the strengths that have permitted them to avoid heavy mathematical derivations in explaining machine learning algorithms so they can help readers from different fields understand algorithms. I strongly recommend this book to all newcomers to data mining, especially to those who wish to understand the fundamentals of machine learning algorithms."--INFORMS Journal of Computing

"The third edition of this practical guide to machine learning and data mining is fully updated to account for technological advances since its previous printing in 2005 and is now even more closely aligned with the use of the Weka open source machine learning, data mining and data modeling application. Beginning with an introduction to data mining, the volume explores basic inputs, outputs and algorithms, the implementation of machine learning schemes and in-depth exploration of the many uses of the Weka data analysis software. Numerous illustration, tables and equations are included throughout and additional resources are available through a companion website. Witten, Frank and Hall are academics with the department of computer science at the University of Waikato, New Zealand, the home of the Weka software project."--Book News, Reference & Research

"I would recommend this book to anyone who is getting started in either data mining or machine learning and wants to learn how the fundamental algorithms work. I liked that the book slowly teaches you the different algorithms piece by piece and that there are also a lot of examples. I plan on taking