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Use the Cluster Discrimination tab to compare two clusters that exist in a clustering model. You can see how different combinations of attributes and values are represented within the clusters.
For More Information: Microsoft Clustering Algorithm, Browse a Model Using the Microsoft Cluster Viewer
Options
Refresh viewer content
Reload the mining model in the viewer.
Mining Model
Choose a mining model from those in the current mining structure. The mining model will open in its associated viewer.
Viewer
Choose a viewer to use to explore the selected mining model. You can use the custom viewer for clustering models, or the Microsoft Mining Content Viewer. You can also use plug-in viewers if available.
Cluster 1
Select a cluster, so that you can compare it to another cluster.
Cluster 2
Select a second cluster from the list of clusters in the mining model, to compare to Cluster 1. You can also compare a cluster to its complement, meaning all cases in the model except those in the selected cluster.
Discrimination scores for <cluster 1> and <cluster 2>
The columns in the graph provide information about how each attribute-value pair is related to the two selected clusters.
| Variables | An attribute in the mining model. |
| Values | A value of the attribute selected in Variables. |
| Favors <cluster 1> | The bar graph on the left represents the probability that the selected attribute-value pair is representative of the cluster selected in Cluster 1. You can pause the mouse over the bar to see the value, represented as a percentage. Note that even if the value is zero, it doesn't mean the attribute-value is necessarily missing from the cluster, just that the distribution strongly favors one cluster over the other. |
| Favors <cluster 2> | The bar graph on the right represents the probability that the selected attribute-value pair is representative of the cluster selected in Cluster 2. |
See Also
Data Mining Algorithms (Analysis Services - Data Mining)
Mining Model Viewers (Data Mining Model Designer)
Data Mining Model Viewers