The Microsoft Cluster Viewer in Microsoft SQL Server Analysis Services displays mining models that are built with the Microsoft Clustering algorithm. The Microsoft Clustering algorithm is a segmentation algorithm for use in exploring data to identify anomalies in the data and to create predictions. For more information about this algorithm, see Microsoft Clustering Algorithm.
To view detailed information about the equations used in the model and the patterns that were discovered, use the Microsoft Generic Content Tree viewer. For more information, see Browse a Model Using the Microsoft Generic Content Tree Viewer or Microsoft Generic Content Tree Viewer (Data Mining).
When you browse a mining model in Analysis Services, the model is displayed on the Mining Model Viewer tab of Data Mining Designer in the appropriate viewer for the model. The Microsoft Cluster Viewer provides the following tabs for use in exploring clustering mining models:
The Cluster Diagram tab of the Microsoft Cluster Viewer displays all the clusters that are in a mining model. The shading of the line that connects one cluster to another represents the strength of the similarity of the clusters. If the shading is light or nonexistent, the clusters are not very similar. As the line becomes darker, the similarity of the links becomes stronger. You can adjust how many lines the viewer shows by adjusting the slider to the right of the clusters. Lowering the slider shows only the strongest links.
By default, the shade represents the population of the cluster. By using the ShadingVariable and State options, you can select which attribute and state pair the shading represents. The darker the shading, the greater the attribute distribution is for a specific state. The distribution decreases as the shading gets lighter.
To rename a cluster, right-click its node and select Rename Cluster. The new name is persisted to the server.
To copy the visible section of the diagram to the Clipboard, click Copy Graph View. To copy the complete diagram, click Copy Entire Graph. You can also zoom in and out by using Zoom In and Zoom Out, or you can fit the diagram to the screen by using Scale Diagram to Fit in Window.
The Cluster Profiles tab provides an overall view of the clusters that the algorithm in your model creates. This view displays each attribute, together with the distribution of the attribute in each cluster. An InfoTip for each cell displays the distribution statistics, and an InfoTip for each column heading displays the cluster population. Discrete attributes are shown as colored bars, and continuous attributes are shown as a diamond chart that represents the mean and standard deviation in each cluster. The Histogram bars option controls the number of bars that are visible in the histogram. If more bars exist than you choose to display, the bars of highest importance are retained, and the remaining bars are grouped together into a gray bucket.
You can change the default names of the clusters, to make the names more descriptive. Rename a cluster by right-clicking its column heading and selecting Rename cluster. You can also hide clusters by selecting Hide column.
To open a window that provides a larger, more detailed view of the clusters, double-click either a cell in the States column or a histogram in the viewer.
Click a column heading to sort the attributes in order of importance for that cluster. You can also drag columns to reorder them in the viewer.
To use the Cluster Characteristics tab, select a cluster from the Cluster list. After you select a cluster, you can examine the characteristics that make up that specific cluster. The attributes that the cluster contains are listed in the Variables columns, and the state of the listed attribute is listed in the Values column. Attribute states are listed in order of importance, described by the probability that they will appear in the cluster. The probability is shown in the Probability column.
You can use the Cluster Discrimination tab to compare attributes between two clusters. Use the Cluster 1 and Cluster 2 lists to select the clusters to compare. The viewer determines the most important differences between the clusters, and displays the attribute states that are associated with the differences, in order of importance. A bar to the right of the attribute shows which cluster the state favors, and the size of the bar shows how strongly the state favors the cluster.