Data Mining Enhancements (SSAS)

Microsoft SQL Server 2005 Analysis Services (SSAS) includes the following data mining enhancements and new features.

Microsoft Naive Bayes Algorithm

The Microsoft Naive Bayes algorithm is a classification algorithm that is quick to build and that works well for predictive modeling. This algorithm is a good option for exploring the data between input columns and predictable columns, and for discovering the relationships between these columns. For more information, see Microsoft Naive Bayes Algorithm.

Microsoft Association Algorithm

The Microsoft Association algorithm builds rules that describe which items are most likely to appear together in a transaction. You can use the rules to predict the presence of an item based on the presence of other items in a transaction. For more information, see Microsoft Association Algorithm.

Microsoft Sequence Clustering Algorithm

The Microsoft Sequence Clustering algorithm, a combination of sequence analysis and clustering, identifies clusters of similarly ordered events in a sequence. You can use the clusters to predict the likely ordering of events in a sequence based on known characteristics. For more information, see Microsoft Sequence Clustering Algorithm.

Microsoft Time Series Algorithm

The Microsoft Time Series algorithm uses a linear regression decision tree approach to analyze time-related data, such as monthly sales data or yearly profits. You can use the patterns that the algorithm discovers to predict values for future time steps. For more information, see Microsoft Time Series Algorithm.

Microsoft Neural Network Algorithm

The Microsoft Neural Network algorithm creates classification and regression mining models by constructing a multilayer perceptron network of neurons, providing support for nonlinear models that are too complex to derive by using other algorithms. For more information, see Microsoft Neural Network Algorithm (SSAS).

Microsoft Logistic Regression Algorithm

The Microsoft Logistic Regression algorithm provides logistic regression support for more business flexibility. For more information, see Microsoft Logistic Regression Algorithm.

Microsoft Decision Trees Algorithm Enhancements

You can now use the Microsoft Decision Trees algorithm with a continuous attribute, such as time, as a predictable column. For more information, see Microsoft Decision Trees Algorithm.

Microsoft Linear Regression Algorithm

The Microsoft Linear Regression algorithm provides linear regression support for more business flexibility. For more information, see Microsoft Linear Regression Algorithm.

Mining Model Wizard

The Data Mining Wizard defines a mining structure and mining model for an Analysis Services project. You can use the wizard to create new mining structures based on either relational or multidimensional data that can be modified later by using Data Mining Designer. For more information, see Data Mining Wizard.

Data Mining Designer

You can use Data Mining Designer in Business Intelligence Development Studio to modify the mining structure and any mining models that you defined in the Data Mining Wizard. You can also use Data Mining Designer to create additional mining models based on the mining structure, to browse existing mining models by using viewers, to compare mining models, and to build predictions based on the mining models. For more information, see Data Mining Designer.

SQL Server Integration Services Support

Several tasks have been added to Microsoft SQL Server 2005 Integration Services (SSIS) that can be used to create a complete data mining solution. By using Integration Services transformations, you can modify data before you create a mining model, create and process mining models, and run prediction queries against existing data mining models.

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