16.3. Data Mining Algorithms in SQL Server Analysis Services 2008

Analysis Services 2008 provides you with nine data mining algorithms that you can utilize to solve various business problems. These algorithms can be broadly classified into five categories based on the nature of the business problem they can be applied to. They are:

  • Classification

  • Regression

  • Segmentation

  • Sequence analysis

  • Association

Classification data mining algorithms help solve business problems such as identifying the type of membership (Platinum, Gold, Silver, Bronze) a new customer should receive or whether the requested loan can be approved for a customer based on his or her attributes. Classification algorithms predict one or more discrete variables based on the attributes of the input data. Discrete variables are variables that contain a limited set of values. Some examples of discrete variables are gender, number of children in a house, and number of cars owned by a customer.

Regression algorithms are similar to classification algorithms; instead of predicting discrete attributes, however, they predict one or more continuous variables. Continuous variables are variables that can have many values. Examples of continuous variables are yearly income, age of a person, and commute distance to work. Algorithms belonging to the regression category should be provided with at least one input attribute that is of type continuous. For example, assume you want to predict the sale price of your house, a continuous value, ...

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