Data Mining with Decision Trees: Theroy and Applications (Machine Perception and Artificial Intelligence) (Hardcover)

by Lior Rokach (Author), Oded Maimon (Author)
Key Phrases: decision forests, pruning trees, reduced error pruning, Decision Trees, Data Mining, Evaluation of Classification Trees (more...)

Editorial Reviews

Product Description
This is the first comprehensive book dedicated entirely to the field of decision trees in data mining and covers all aspects of this important technique. Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining, the science and technology of exploring large and complex bodies of data in order to discover useful patterns. The area is of great importance because it enables modeling and knowledge extraction from the abundance of data available. Both theoreticians and practitioners are continually seeking techniques to make the process more efficient, cost-effective and accurate. Decision trees, originally implemented in decision theory and statistics, are highly effective tools in other areas such as data mining, text mining, information extraction, machine learning, and pattern recognition.This book invites readers to explore the many benefits in data mining that decision trees offer: self-explanatory and easy to follow when compacted; able to handle a variety of input data: nominal, numeric and textual; able to process datasets that may have errors or missing values; high predictive performance for a relatively small computational effort; available in many data mining packages over a variety of platforms; and, useful for various tasks, such as classification, regression, clustering and feature selection.

Product Details

• Hardcover: 244 pages
• Publisher: World Scientific Publishing Company (March 2008)
• Language: English
• ISBN-10: 9812771719
• ISBN-13: 978-9812771711
• Product Dimensions: 9 x 6.1 x 0.8 inches
• Shipping Weight: 1.2 pounds (View shipping rates and policies)
• Average Customer Review: 3.0 out of 5 stars See all reviews (2 customer reviews)
• Amazon.com Sales Rank: #821,363 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

3 of 3 people found the following review helpful:

3.0 out of 5 stars Specialists should consider it, Practitioners should look elsewhere, May 3, 2008

By	Keith McCormick (North Carolina, USA) - See all my reviews (REAL NAME)

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

3.0 out of 5 stars Survey of the literature, not a standalone work, March 31, 2009

By	D. Wilson - See all my reviews (REAL NAME)

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.