- Series: The Morgan Kaufmann Series in Data Management Systems
- Paperback: 664 pages
- Publisher: Morgan Kaufmann; 3 edition (January 20, 2011)
- Language: English
- ISBN-10: 0123748569
- ISBN-13: 978-0123748560
- Product Dimensions: 7.5 x 1.5 x 9.2 inches
- Shipping Weight: 3.1 pounds
- Average Customer Review: 78 customer reviews
- Amazon Best Sellers Rank: #489,784 in Books (See Top 100 in Books)
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Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition
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"...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 a machine learning course this upcoming fall semester and feel that the book gave me great insight that the course will be based on mathematics more than I had originally expected. My favorite part of the book was the last chapter where it explains how you can solve different practical data mining scenarios using the different algorithms. If there were more chapters like the last one, the book would have been perfect. This book might not be that useful if you do not plan on using the Weka software or if you are already familiar with the various machine learning algorithms. Overall, Data Mining: Practical Machine Learning Tools and Techniques is a great book to learn about the core concepts of data mining and the Weka software suite."--ACM SIGSOFT Software Engineering Notes
"This book is a must-read for every aspiring data mining analyst. Its many examples and the technical background it imparts would be a unique and welcome addition to the bookshelf of any graduate or advanced undergraduate student. The book is written for both academic and application-oriented readers, and I strongly recommend it to any reader working in the area of machine learning and data mining."--Computing Reviews.com
From the Back Cover
Data Mining: Practical Machine Learning Tools and Techniques 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. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research.
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There's very little actual math or theory in this book. The average explanation amounts to "There's a technique called X, where you do this... it has a couple problems, but you could try fixing them in these ways." It's great for getting a lot of machine learning and data mining ideas in your head without having to get confused by learning the math behind them.
Problems mostly come from the lack of organization. Most of these are in Chapter 6, which is by far the most important chapter. For instance, this chapter begins with two or three pages describing what's going on in Figure 1.3 from two-hundred pages earlier. Each section of the chapter references its corresponding section in Chapter 4 a lot. The authors also assume that you memorized, in intimate detail, their examples in the first five pages because they keep referencing them in detail throughout the book. Finally, the explanations of a couple algorithms -- decision trees, in particular -- can get disorganized and confusing; however, these are exceptions to the rule.
But, this is a good book. I got a lot of new ideas out of it for how to improve some the algorithms I work on, or for new things to try. It's great to have explanations of these machine learning algorithms and concepts that give you an intuition for what their goal/purpose is without going into too much detail about why they work -- there are ten other books for that.
- It doesn't jump into algorithms with mathematical details. It starts with what is it all about, what input and output look like in typical machine learning problems.
- One point that I really liked is that the book gives algorithms in two chapters (chapter 4 and 6). The first chapter is about basics and latter one gives detail about these algorithms.
- It also covers well that I think it is mostly ignored by other books/tutorials: practical issues. How to normalize data, what happens your data have both categorical and numerical features, discretizing numerical features and so on.
- If you consider using Weka, you should have this book. Authors are from the team who built Weka. For each algorithm described in the book, corresponding names of implementations in Weka are given too. With the book it is easier to understand parameters of Weka implementations of algorithms. Also last part of the book is like extensive Weka tutorial.
- In a few points, the book contains unnecessary details, although it is not the case for overall of the book. One of such things that I remember is chapter 4.7. The book spends 5 whole pages to how to find nearest neighbor efficiently (not-easy stuff), which I think it is really implementation detail. Instead of it, it could explain what nearest neighbor is, or something else.
- The part about Weka has several figures, mostly Weka screen shots. It was difficult to follow these figures, because of black-white screen shots. I think these figures should be in color in the next edition, which will make much easier to follow.
There seems to be so much hype on "data science" these days, when actuaries were doing this stuff with slide rules decades ago.
This book removes the mystery and explains it clearly....
An understanding of data architecture and some math would be helpful, but I think anyone with a technical background would benefit from it.