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. Using SAS Enterprise Miner, this example-driven guide illustrates the application and operation of decision trees in data mining, business intelligence, 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 other business intelligence applications such as OLAP cubes; in addition, it explains other data mining approaches, like regression, cluster analysis, and neural networks. The book contains examples that illustrate 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. Many of the examples are drawn from purchase behavior, risk assessment, and business-to-business marketing. The target audience for this book includes analysts who already 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 data mining and business intelligence. Using SAS Enterprise Miner, this book illustrates the application and operation of decision trees in business intelligence, data mining, business analytics, prediction, and knowledge discovery. Examples show how various aspects of decision trees are constructed, how they operate, and how to interpret them.
