Using SAS Enterprise Miner, Barry de Ville's Decision Trees for Business Intelligence and Data Mining illustrates the application and operation of decision trees in business intelligence, data mining, business analytics, prediction, and knowledge discovery. It explains in detail the use of decision trees as a data mining technique and how this technique complements and supplements data mining approaches such as regression, as well as other business intelligence applications that incorporate tabular reports, OLAP, or multidimensional cubes. Examples show how various aspects of decision trees are constructed, how they operate, how to interpret them, and how to use them in a range of predictive and descriptive applications. The examples are drawn from the areas of purchase behavior, risk assessment, and business-to-business marketing.

This book also describes the various disciplines that contributed to the development of decision trees and how, even today, decision trees can be used as a form of machine intelligence. Examples of using and interpreting graphic decision trees as executable rules are provided.

The target audience includes analysts who have an introductory understanding of data mining and who want to benefit from a more advanced, in-depth look at the theory and methods of a decision tree approach to business intelligence and data mining.

Amazon.com® Reader Reviews (Ranked by Helpfulness)

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For descent understanding of Decision Trees using SAS - 2007-03-14
Reviewer Rating:
A good book to understand decision trees using SAS e-miner. I wish it could have more literature on the splitting algorithms i.e. Gini, Entropy, Chisquare.
The book along with SAS data mining material or Data Mining book by Larose is a good resource to understand Decision Tree.

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