Databases Reference
In-Depth Information
Figure 14-33
Once the processing is complete, the mining model editor switches to the
mining model view where you can see the various clusters created based on
the chosen attributes from the cube. There are 10 clusters created by default
and you will find the strongest relationship between clusters cluster4 and
cluster10. Similar to analyzing the relational mining model clusters, you can
use the cluster profiles, cluster characteristics, and cluster discrimination tabs
to learn more about the clusters created from the cube.
Analyzing the Cube with a Data Mining Dimension
When you created the OLAP Mining Model you selected creation of a data
mining dimension and a cube in the data mining wizard. In order to create a
new dimension and cube you need a DSV. Hence a DSV that includes a
query to the OLAP mining model to retrieve the data from mining model is
created. You will see a DSV called Dim Customer_DMDSV created under the
DSV folder in the solution explorer. Next a dimension called [Dim Cus-
tomer_DMDim] that includes attributes from the data mining model is created.
Finally a cube called [Adventure Works DW_DM] is created that includes all
the cube dimensions and measure groups of the Adventure Works DW cube
but also includes the newly created data mining dimension [Dim Cus-
tomer_DMDim]. In
Chapter 9
you learnt the data mining relationship between
a dimension and cube. The data mining relationship is defined between a di-
mension derived from a data mining model and a cube that contains it. The
cube [Adventure Works DW_DM] that was created by the data mining wizard
includes a data mining dimension. If you open the Dimension Usage tab of
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