By Jamie MacLennan

Know how to take advantage of the hot positive aspects of Microsoft SQL Server 2008 for info mining through the use of the instruments in info Mining with Microsoft SQL Server 2008 , as a way to assist you use the SQL Server information Mining Toolset with place of work 2007 to mine and study information. discover all of the significant facts mining algorithms, together with naive bayes, determination timber, time sequence, clustering, organization ideas, and neural networks. study extra approximately themes like mining OLAP databases, information mining with SQL Server Integration prone 2008, and utilizing Microsoft facts mining to unravel company research difficulties.

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Another influencer (relatively about half as important as Region) is a commute distance of 5 - 10 Miles. The number of Cars, the Age, and the number of Children are also influencers, but to a lesser degree. The next section of the report explains the influencers for the Clerical value of the Occupation column, and should be interpreted similarly. tex V2 - 10/04/2008 2:00am The Analyze Key Influencers Tool When interpreting the report, keep in mind that, although the bar lengths may suggest otherwise, you should not assume that an Income of 39000 71062 influences a Skilled Manual occupation exactly as much as an income of <39050 influences a Clerical occupation.

Business problems such as churn analysis, risk management, and targeted advertising usually involve classification. Classification is the act of assigning a category to each case. Each case contains a set of attributes, one of which is the class attribute. The task requires finding a model that describes the class attribute as a function of input attributes. In the College Plans data set shown in Figure 1-1, the class is the CollegePlans attribute with two states: Yes and No. A classification model will use the other attributes of a case (the input attributes) to determine patterns about the class (the output attribute).

The accuracy of an algorithm depends on the nature of the data. For example, a decision tree algorithm is usually a very good choice for any classifications. However, if the relationships among attributes are complicated, a neural network may perform better. A good approach is to build multiple models using different algorithms, and then compare the accuracy of these models. Even with a single algorithm, you can tune the parameter settings to optimize the model accuracy. Model Assessment In the model assessment stage, you use tools to determine the accuracy of the models that were created, and you examine the models to determine the meaning of discovered patterns and how they apply to your business.

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