Decision Trees for Analytics Using SAS Enterprise Miner

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9781612903156: Decision Trees for Analytics Using SAS Enterprise Miner

Decision Trees for Analytics Using SAS Enterprise Miner is the most comprehensive treatment of decision tree theory, use, and applications available in one easy-to-access place. This book 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.

An expanded and enhanced release of Decision Trees for Business Intelligence and Data Mining Using SAS Enterprise Miner, this book adds up-to-date treatments of boosting and high-performance forest approaches and rule induction. There is a dedicated section on the most recent findings related to bias reduction in variable selection. It provides an exhaustive treatment of the end-to-end process of decision tree construction and the respective considerations and algorithms, and it includes discussions of key issues in decision tree practice.

Analysts who have an introductory understanding of data mining and who are looking for a more advanced, in-depth look at the theory and methods of a decision tree approach to business intelligence and data mining will benefit from this book.

This book is part of the SAS Press program.

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About the Author:

Barry de Ville is a Solutions Architect at SAS. His work with decision trees has been featured during several SAS users' conferences and has led to the award of a U.S. patent on "bottom-up" decision trees. Previously, he led the development of the KnowledgeSEEKER decision tree package. He has given workshops and tutorials on decision trees at such organizations as Statistics Canada, the American Marketing Association, the IEEE, and the Direct Marketing Association.

Padraic Neville is a Principal Research Statistician Developer at SAS. He developed the decision tree and boosting procedures in SAS Enterprise Miner and the high-performance procedure HPFOREST. In 1984, Padraic produced the first commercial release of the Classification and Regression Trees software by Breiman, Friedman, Olshen, and Stone. He since has taught decision trees at the Joint Statistical Meetings. Neville's current research pursues better insight and prediction with multiple trees.


"Decision Trees for Analytics Using SAS Enterprise Miner is an excellent book for practitioners and project managers alike. With ample figures and examples, this book clearly illustrates and explains the roles and concepts that decision trees play in descriptive, predictive, and explanatory analyses.

A salient and unique feature of this book is that it explicitly connects decision trees to business intelligence and elaborates on how decision trees can be used together with other data mining methods. I enjoyed the opportunity to review this very interesting book." --Huan Liu, Professor, Computer Science and Engineering, Arizona State University

"This book has changed the way I think about decision trees and will allow me to take my organization's applied analytics and business intelligence initiatives to the next level. It may sound like a cliché, but I might describe this book as providing a roadmap to everything I have wanted to accomplish using decision trees, but was afraid to try.

Armed with knowledge from de Ville and Neville, I now feel like I have much more flexibility to interactively grow a tree without apology; as opposed to simply running an algorithm and hoping the results are interpretable, relevant, and informative.

This book offers an excellent blend of history, theory, and application of decision trees, as well as a great comparison of trees with OLAP cubes and BI tools as well as regression techniques. It is a well-balanced blend of theory and application.

I would recommend this book to anyone experienced in data mining and predictive modeling, new to decision trees, or wanting more details about their specific use and implementation in SAS products." --Matt Bogard, Market Research Coordinator, Office of Institutional Research, Western Kentucky University, Adjunct Instructor, Department of Economics, Western Kentucky University

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