Download Decision Trees for Business Intelligence and Data Mining: by Barry deVille PDF

By Barry deVille

Utilizing SAS company Miner, Barry de Ville's determination bushes for enterprise Intelligence and information Mining illustrates the applying and operation of determination timber in company intelligence, information mining, enterprise analytics, prediction, and information discovery. It explains intimately using selection timber as a knowledge mining approach and the way this method enhances and vitamins facts mining techniques equivalent to regression, in addition to different company intelligence functions that comprise tabular experiences, OLAP, or multidimensional cubes. Examples exhibit how numerous facets of selection timber are built, how they function, the best way to interpret them, and the way to exploit them in various predictive and descriptive functions. The examples are drawn from the parts of buy habit, chance overview, and business-to-business advertising and marketing. This booklet additionally describes a few of the disciplines that contributed to the advance of determination bushes and the way, even at the present time, choice timber can be utilized as a kind of computer intelligence. Examples of utilizing and studying photograph determination bushes as executable principles are supplied. the objective viewers comprises analysts who've an introductory knowing of information mining and who are looking to reap the benefits of a extra complex, in-depth examine the idea and strategies of a choice tree method of enterprise intelligence and knowledge mining.

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Extra resources for Decision Trees for Business Intelligence and Data Mining: Using SAS Enterprise Miner

Example text

To understand the scenario presented in this article, assume that you are a marketing manager for a software development/publishing company and that you are evaluating the effects of various promotional programs on long-term software retention. 1 1 Figures presented in this example are, in general, the same as those in the original article. The variable names and scenario have been changed to reflect a marketing application instead of the epidemiological research application that was featured in the original article.

This type of analysis technique proved to be very popular. The publication of Morgan and Sonquist’s results, coupled with the availability of decision tree software (both in the United States and Europe), led to the development of decision trees as a stand-alone analysis technique. For many analysts, including statistical analysts, it became simpler and just as effective to use decision trees alone, which avoided the requirement of respecifying the regression equation. This decision tree popularity coincided with the growing power of computers and the ability of statistical analysts to move out of a tabular analysis framework and into a regression framework, or into a regression-augmentedwith-AID framework.

15 This test of significance determines which values are combined. The values are used as the various inputs that are considered as splitting criteria in the construction of the decision tree. To elaborate, a test of significance determines whether two values are the same with respect to their relationship to the target. If their values are the same, then they are combined. If their values are different, then they are separate branches on the decision 38 Decision Trees for Business Intelligence and Data Mining: Using SAS Enterprise Miner tree.

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