The Clustering Analysis view groups cases in the model in a way that the cases inside a group are similar to each other (e.g. cases have the same case attribute values are in the same group). See this Wikipedia article for more about the idea behind clustering.
You can use the Clustering Analysis View, for example, to check data integrity. That is, the Clustering Analysis might reveal that the model actually contains data from two different processes.
You can use the left panel to filter cases. Note that you are not bound to using just the Flowchart analysis, as you can change the analysis by right-clicking the analysis and selecting a different type of analysis shown on the panel.
The right panel contains the clustering analysis. The table shows the clusters, how many cases are in each cluster, and the following details for each cluster:
- Feature and Value: These two columns list the case attributes' and other values that are common to the cases in the cluster.
- Cases (tot): the total number of cases having the value shown on the row.
- Density (tot): value in "Cases (tot)" divided by the total number of cases * 100.
- Cases (cl): the number of cases having the value shown on the row in this particular cluster.
- Density (cl): the value in "Cases (cl)" divided by the number of cases in the cluster * 100.