Rating: 
Summary: Promotes a deep understanding of the topic
Review: This book is excellent for anyone entering the fields of data mining or machine learning. The material is organised into functions rather than techniques, which promotes a deeper understanding of why different approaches work, when to use them, and how they can be combined to maximise results.For those already conversant in machine learning, it contains a wealth of practical techniques for improving and analysing results. I expect to use it often in the course of my research.
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Summary: You HAVE to read this book!
Review: This book is THE best book I have read about data mining. And I have read most of them (see ISBNs: 0070057796, 0471253847, 0262560976, 0201403803, 0471179809, 013743980, 0137564120, 1558605290, 1558604030). It is fresh, clear, well balanced. If your native language is not English, then you should definetly read THIS book first. The feature that is the most important for me is "just enough statistics". That is, you can understand the processes & descriptions even if you have not wasted your life and youth studying statistics; what is needed of it to understand is given shortly and very well. Many other books are too deep or too shallow (like Berry's, which is a good introduction, but nothing more than that). If the rating was scaled 1-6 stars, I'd give this book a 10.
Rating: 
Summary: explains what you will read elsewhere
Review: This is a nice book which explains in easy-to-understand English some of the concepts that you might read elsewhere in papers, etc. I can't comment on the usefulness of this book in a non-research or "totally business" type of environment, but for the industry/academic hybrid researcher, this book is recommended. I wish I could add it to my amazon Data Mining list, but lists can't be edited at this time.
Rating: 
Summary: lengthy words just to make it called a book
Review: This is the worst book I have ever ordered from Amazon. It seems to me that the author does not know much on the practide. The books have make some points in inserting buzz words occasionaly, but no more than that. The rest is just full of words that you have no clue why they make it meaninglessly lengthy -- just to make it long enough to be called a book?
Rating:
Summary: Excellent introduction to data mining algorithms
Review: Witten and Frank have generated a book that is readable without eliminating all technical (yes, even mathematical!) descriptions of the key data mining algorithms. And they are up-to-date, including support vector machines and boosting. There are sufficient examples of the techniques to provide readers with a good feel for what each technique can accomplish. For example, how many books can provide a readable explanation of support vector machines? There are some quibbles, such as not including any discussion of neural networks (noted in Ch. 1 with another reference)--I believe it deserves some attention because of its widespread use. Additionally, future editions should include a least a brief summary of data preprocessing, input selection, feature creation, etc. But these are quibbles. The Java portion of the book is not of as much interest to me, but for those wishing to implement the algorithms, it provides a nice blueprint (from the code I looked at). For what they have undertaken, they have performed admirably, and I would highly recommend this book.
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