By Barry deVille
Utilizing SAS firm Miner, Barry de Ville's selection timber for enterprise Intelligence and information Mining illustrates the appliance and operation of choice bushes in enterprise intelligence, facts mining, enterprise analytics, prediction, and data discovery. It explains intimately using selection timber as an information mining strategy and the way this method enhances and supplementations facts mining methods comparable to regression, in addition to different enterprise intelligence functions that comprise tabular reviews, OLAP, or multidimensional cubes. Examples express how quite a few elements of determination bushes are developed, how they function, how one can interpret them, and the way to exploit them in a variety of predictive and descriptive functions. The examples are drawn from the parts of buy habit, threat overview, and business-to-business advertising. This publication additionally describes some of the disciplines that contributed to the improvement of determination timber and the way, even this present day, determination timber can be utilized as a kind of computer intelligence. Examples of utilizing and reading photo determination timber as executable principles are supplied. the objective viewers comprises analysts who've an introductory realizing of knowledge mining and who are looking to reap the benefits of a extra complicated, in-depth examine the speculation and techniques of a choice tree method of company intelligence and information mining.
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Extra info for Decision Trees for Business Intelligence and Data Mining: Using SAS Enterprise Miner
Because data sets are usually more structured than books, they are a desirable source of knowledge for machine learning applications. All decision trees are collections of rules. Although decision trees appear to be visual representations, if you look underneath, you will see that decision trees are rule expressions. Thus, every branch on the decision tree has a semantic description and because of this, decision trees are natural forms of machine learning. The development of decision trees to form rules is called rule induction in machine learning literature.
60 A Brief Review of the SAS Enterprise Miner ARBORETUM Procedure.................................................................... 60 Introduction In data analysis, it is common to work with data with descriptive, predictive, or explanatory outcomes in mind. A descriptive analysis could simply display a relationship in data or it could display the relationship as a graphic, such as a bar chart. The goal is to describe the data or a relationship among various data elements in the data set. This is common and normally the baseline point of departure in working with data to develop insight.
14. The degree of separation between two groups can be used as a test of the difference between two groups. The larger the separation, the stronger the relationship and, consequently, the greater the statistical confidence in the relationship. Because any two nodes on the branch of a decision tree can be seen as two groups, the internode separation can be tested with a test of significance. Multi-node tests can be used just as multigroup tests are used. 14: Illustration of Tests of Significance The second way that CHAID methods use statistics is to judge which relationships are strong enough to use in building the model.