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DATA MINING WITH DECISION TREES: THEORY AND APPLICATIONS (Series in Machine Perception & Artifical Intelligence)
 
 
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DATA MINING WITH DECISION TREES: THEORY AND APPLICATIONS (Series in Machine Perception & Artifical Intelligence) (Hardcover)

by ROKACH LIOR ET AL (Author)
1.0 out of 5 stars See all reviews (1 customer review)
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Product details

  • Hardcover: 244 pages
  • Publisher: World Scientific Publishing (17 Feb 2008)
  • Language English
  • ISBN-10: 9812771719
  • ISBN-13: 978-9812771711
  • Product Dimensions: 22.8 x 15.4 x 2 cm
  • Average Customer Review: 1.0 out of 5 stars See all reviews (1 customer review)
  • Amazon.co.uk Sales Rank: 851,978 in Books (See Bestsellers in Books)

    Popular in these categories:

    #48 in  Books > Computing & Internet > Computer Science > Algorithms > Fuzzy Logic
    #61 in  Books > Computing & Internet > Databases > Data Storage & Management > Data Mining

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Product Description

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.
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1 of 1 people found the following review helpful:
1.0 out of 5 stars Very disappointing, an extended survey paper! , 6 May 2008
I have to say that this book was very disappointing. It promised to be the "first comprehensive book dedicated entirely to the field of decision trees".

The book is best described as "an extended survey paper" on the subject. For example ID3, C4.5, CART, is each given one paragraph of few lines in a chapter called "Advanced Decision trees"!

However, decision forests (random forests) is given the longest chapter in the book. So, it seems that this is the subject really familiar to the authors, and not the rest, so that the authors took the easy way out and decided to tell you the titles, and a pointer to a reference.

Examples are over-simplified, description of algorithms is very sketchy, nothing is derived, no proofs for theorists, no real examples or implementation for practitioners. So, if you are new to the field, you will NOT learn much.

This book is definitely a waste of good £42 (I bought it on Amazon UK), so save your money and do a literature survey yourself in your library - which is almost the only good thing about the book (the bibliography)- and if you are a practitioner like me, it's totally useless.
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