Amazon.com: Customer Reviews: Data Mining with Decision Trees: Theory and Applications (Series in Machine Perception and Artifical Intelligence)
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on March 31, 2009
The important thing to know about this book before purchasing is that it does not, on the whole, stand on its own. It covers a great number of topics relating to decision trees and their use, but the coverage is primarily as a survey of the literature rather than as usage examples or algorithmic details. Most of the book takes a very qualitative look at the topics; there are few if any quantitative results to be found within.

If you're looking for a collection of organized references to important papers on the topic of decision trees and you've access to the archives of the cited journals, then this book is useful as a jumping-off point to see how the various papers relate. If you're looking for a standalone book on the topic, look elsewhere.
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on May 3, 2008
I will recommend this to one or two colleagues, but it will not be something I will recommend to clients.

The first thing you notice about this book is its very academic style. It has numbered paragraphs like 2.0, and 7.3.1.12. It been used a graduate text, presumably for mathematicians and computer scientists. I think it would be good for that purpose. It could work quite well for statisticians that are interested in the details of data mining algorithms. It is in a series in Machine Perception and Artificial Intelligence. Other titles include "Fundamentals of Robotics", and "Bridging the Gap Between Graph Edit Distance and Kernel Machines", so don't confuse this book with something like Data Mining Techniques, which is written for a general audience. It opens the 2nd chapter with (condensed): "A training set is a bag instance of a bag schema. A bag instance is a collection of tuples that may contain duplicates." The folks that I work with can instantly divide themselves into those that would consider a book like this, and those that wouldn't. It cites references in almost every sentence, which can be distracting to the casual reader, and eventually convinced me that I need to read the original authors like Breiman. Classification and Regression Trees

So having issued a warning, there is plenty to like. The authors have made a real attempt to cover everything - I found 1/3 that I knew, 1/3 that will be quite useful to me, and 1/3 that is too much detail for me. Chapter 3 "Evaluation of Classification Trees" will be great for statisticians that wondered how to judge the efficacy of a tree that was built without hypothesis testing. Also, I was very pleased to see a chapter on "Decision Forests", which is a discussion of "ensemble methods" - in other words combining a set of tree models.

I was hoping for something that would have a detailed chapter on each of the most common decision trees algorithms with briefer sections on the obscure ones. It has all this information, but in a way that I have to work pretty hard to get to it. If you want a quick overview of data mining (even if you think that trees are the method you are going to use), try Data Mining Techniques. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management If you want to know the details, but are content to learn the details only on the well known techniques (like CHAID and CART) then Larose is a good choice. Discovering Knowledge in Data: An Introduction to Data Mining
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