Topic Status: Some information in this topic is preview and subject to change in future releases. Preview information describes new features or changes to existing features in Microsoft SQL Server 2016 Community Technology Preview 2 (CTP2).
The Cluster Diagram tab provides a graphical view of all the clusters that the clustering model contains.
Choose a mining model, from those in the current mining structure. The mining model will open in its associated viewer.
Choose a viewer to explore the selected mining model. You can use one of the custom clustering viewers, or use the Microsoft Mining Content Viewer. You can also use a plug-in viewer if available.
Zoom in to the diagram, to get a detailed view of the clusters.
Zoom out from the diagram, to see more clusters.
Copy Graph View
Copy the visible section of the diagram to the clipboard.
Copy Entire Graph
Copy the complete diagram to the clipboard.
Scale diagram to fit window
Zoom out from the diagram until the whole diagram fits within the screen.
Opens the Find Node dialog box. This feature is useful in large models, where it can be hard to find the attribute of interest. You can enter search criteria in the dialog box and the view of the clusters will be filtered to show only the cluster containing the search string.
Reorder the clusters in the diagram to improve the layout.
Use this option to change which attribute-value pairs are displayed in the cluster diagram. You use the Shading Variable option to select an attribute, and use State to choose a value. The shading in the graph indicates the density of that attribute-value pair within the cluster.
If Population is selected, the diagram shows the amount of support for each cluster, meaning the number of cases since no attribute is selected.
Select an attribute to represent in the cluster diagram.
Select a single state of the Shading Variable to use in the cluster diagram.
Adjust how many links are shown between clusters, by moving the slider up or down. Lowering the slider leaves only the strongest associations between clusters.