Cluster Discrimination Tab (Mining Model Viewer)

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

Reference

Mining Model Viewers (Data Mining Model Designer)

Concepts

Data Mining Algorithms (Analysis Services - Data Mining)

Data Mining Model Viewers