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Data Mining Dimension

A DM dimension is a dimension with a special parent-child hierarchy that's based on relationships discovered in your data by applying data mining, as opposed to a regular dimension where the hierarchies are user-defined. For example, you might discover interesting groups of customers by building a mining model that applies the Microsoft_Clustering algorithm on demographic data in your Customers dimension. A DM dimension based on this mining model can be used to browse your customer sales data and slice it by the customer groups found by the mining model.

A data mining dimension is processed with a data source view that points to a DMX query which fetches data from an OLAP-specific view of the source mining model's content.

You can build data mining dimensions based on OLAP mining models.

Although data mining dimensions are shared dimensions, they differ from other types of shared dimensions in several ways. Unlike other types of shared dimensions, data mining dimensions cannot be created in Dimension Editor, and they must be based on OLAP data mining models. Furthermore, they cannot be edited after they have been created, they do not support dimension security through database or cube roles, and they can be included only in virtual cubes.

Data mining dimensions can be created in either the Dimension Wizard or the Mining Model Wizard. If you use the Dimension Wizard to create your data mining dimension, you can use an existing OLAP mining model. If you use the Mining Model Wizard to create a data mining dimension, you can create the new dimension at the same time that you create the new OLAP mining model it is based on. When creating a dimension with the Mining Model Wizard, you also have the option of creating a virtual cube to contain the new data mining dimension and the source cube of the mining model. If you choose not to create a virtual cube to contain the data mining dimension while you are in the Mining Model Wizard, you can use the Virtual Cube Wizard later to add it to an existing or new virtual cube. To view the members of a data mining dimension, use Dimension Browser. Excluding the top node of a mining model that is based on either the Microsoft® Decision Trees or the Microsoft Clustering algorithm, each level member of the dimension represents the rule corresponding to a node in the mining model. You can view the Multidimensional Expressions (MDX) statement used to generate the rule in the custom member formulas pane of Dimension Browser.

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analyse/data-mining-dimension.txt · Zuletzt geändert: 2016/06/24 16:36 von molschimke