- Publisher: Hanser Fachbuch (January 1, 2001)
- Language: German
- ISBN-10: 3446215336
- ISBN-13: 978-3446215337
- Product Dimensions: 6.6 x 0.9 x 9.5 inches
- Shipping Weight: 1.7 pounds
- Average Customer Review: 38 customer reviews
- Amazon Best Sellers Rank: #17,665,050 in Books (See Top 100 in Books)
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Data Mining. (German) Paperback – January 1, 2001
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“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 --This text refers to an out of print or unavailable edition of this title.
Highly anticipated second edition of the highly-acclaimed reference on data mining and machine learning. --This text refers to an out of print or unavailable edition of this title.
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It should be pointed out that about 10% of the text of this book is devoted simply as a user manual for an open source MLA package called Weka. When I first realized this I almost flipped; I really didn't want a book that was devoted to gaining a surface understanding of a particular implementation of a set of algorithms. After reading through, I can tell you it is not. All the algorithms are explained well enough that you could implement them and work out simple examples on paper.
I should note also that Weka, as well as a lot of the algorithms in this book, don't parallelize well (or obviously). This is an excellent point to get your feet wet and do some exploratory analysis, but if you're past that point and want to learn about crunching big (TB+) data you should look elsewhere.
One area that the text does not cover (and, for many software engineers this is not a fault) is some of the mathematics behind some of the algorithms the author proposes. For instance, in the author's description of linear regression using SGD he glosses over the math of actually calculating the gradient by saying "there's a matrix inversion involved and its available in prepackaged software." I'm not saying this is bad, because if you're a good software engineer the first thing you'll do it look for an existing implementation that you can alter to fit your needs, so he's right. It just may not be what mathematicians or more theory-oriented computer scientists expect.
The book has a good coverage of techniques and algorithms, although I was somewhat disappointed that they do not mention Influence Diagrams, considering the amount of coverage of both decision trees and Bayesian techniques. Their discussion of Combining Multiple Models, however, is well done, and is not covered to this extent in most books I've seen. I also like how they broke out the discussion of input and output (knowledge representation) into their own chapters.
Addendum 10/30: After reading a good hunk of this book I still agree with most of what I said earlier, but I do think the authors could have gone into graphical models a lot more. At the end of the discussion on Bayesian networks, Markov networks and other graphical models are mentioned very briefly and the author says they are very big in ML right now, but he doesn't say why they didn't describe them further. It might have something to do with the organization of the book. Graphical models almost need a chapter of their own but the book's chapters discuss all techniques in one chapter but with varying levels of detail.
Obviously, this book is a perfect companion to the Weka machine toolbox, which is quickly becoming a standard, invaluable research toolbox for many.
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