Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations

by Ian H. Witten

Book Overview

This book 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. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining--including both tried-and-true techniques of the past and Java-based methods at the leading...

• Format: Paperback
• Language: English
• ISBN: 1558605525
• ISBN13: 9781558605527
• Release Date: October 1999
• Publisher: Morgan Kaufmann Publishers
• Length: 371 Pages
• Weight: 1.55 lbs.
• Dimensions: 0.8" x 7.3" x 9.1"

Customer Reviews

Thorough, well-written, and crystal-clear explanations.

Highly recommend this book for a practical introduction to the theory and applications of Machine Learning. Great book if you are looking to ACTUALLY implement some machine learning systems, prefer to learn via diagrams, a "how-stuff-works"-style explanation, and skip much of the equations and heavy math that fills similar books. Obviously, this book is a perfect companion to the Weka machine toolbox, which is quickly becoming a standard, invaluable research toolbox for many.

Incredibly practical introduction

This book is perfect if you are trying to get your hands around what data mining and machine learning is. Most of the books I have read on this subject want to start with equations and get more complex from there, with little practicality. This book makes extensive use of examples and introduces the mathematical basis for algorithms where needed. The authors make the point that simpler algoritms often work best for solving machine learning problems. Similarly, I would argue, simpler books work best for understanding highly complex fields. I very highly recommend this book.

Lucid

I'm surprisingly please with this book. I've been reading up on the topic and associated algorithms in other books for some time; I'm a software developer but don't have a statistics background, and so felt a lot of the texts were too focused on the math and the theory while being thin on content when it came to "rubber hitting the road", or even using clear, simple examples and straight-forward notation. This book is so well-written that it communicates the concepts clearly, lucidly and in an organized fashion. The section that introduces Bayesian probability was drop-dead simple to follow. Quite frankly, having read a few other treatments on it, I can now say that everything else I read before this was overly complicated. Brevity is the soul of wit, no? To the reviewer who criticized the authors use of words to describe equations: This is what the authors intended to do. Would you fault them for writing in English if you wanted Greek? Not everyone who can benefit from applied data mining has the requisite background to understand the nitty gritty mathematics, nor should they have to, if they just want to understand the behavior and practical applications of the technology.

Great Book in Every Way

The first edition of this book was good, but this is a huge improvement. The writing is really great, very clear, even when it heads into deeper waters. The explanation, for instance, of the various algorithms for accomplishing attribute discretization is very clear, even as the equations start to get very long and complicated. It's pretty incredible that this book is so readable, kudos to the authors for that. Most importantly, though, it gives you a very good sense of what you need to know as you work through the many data mining options. The authors' assertion that DM is not a magic box is good, and it is clearly a dictate that they mind themselves throughout the book: DM doesn't mean that you just plug in a black box and it starts to lay eggs. Generating rules, building trees and knowing how to pick attributes to build the tree from are all critical topics that get excellent treatment.

Good Book for Data Mining

This is the second edition of the author's Data Mining book. The first part of the book focuses on data mining algorithms, implementation issues, and how to evaluate the results of the data mining model. The second part focuses on the authors "Weka Machine Learning Workbench" which is available under a GNU General Public License. See their web site: http://www.cs.waikato.ac.nz/~ml/weka/index.html for the software. This software appears to be widely used at academic institutions. The first section of the book provides an overview of the algorithms that the software implements. If you need an in depth understanding of the algorithms, you will need additional information sources. If you simply download the software without an understanding of which algorithms are appropriate to your data mining problem, you may become frustrated with the performance, or, even worse, you may misinterpret the results of the data mining model. In general, learning data mining is much more complex than this book (or any other single book) can adequately describe; however, this is an excellent source for someone interested in data mining.