Compare Clusters
Last updated
Last updated
Compare clusters is a tool to identify the optimal number of clusters for K-means Clustering using the Davies-Bouldin index. The Davies-Bouldin index is a measure of cluster quality where a lower value indicates better clustering, i.e., the separation between points within the clusters is low (tight clusters) and separation between clusters is high (distinct clusters).
We recommend normalizing your data prior to running Compare clusters, but the task will run on any counts data node.
Click the counts data node
Click the Exploratory analysis section of the toolbox
Click Compare clusters
Configure the parameters
Click Finish to run (Figure 1)
The parameters for Compare clusters are the same as for K-means clustering.
The Compare clusters task report is an interactive line chart with the number of clusters on the x-axis and the Davies-Bouldin index on the y-axis (Figure 2).
The Compare clusters task report can be used to run K-means clustering.
Click a point on the plot to select it or type the number of clusters in the text box Partition data into clusters
Selecting a point sets it as the number of clusters to partition the data into. The number of clusters with the lowest Davies-Bouldin index value is chosen by default.
Click Generate clusters to run K-means clustering with the selected number of clusters
A K-means clustering task node and a Clustering result data node are produced. Please see our documentation on K-means Clustering for more details.
If you need additional assistance, please visit our support page to submit a help ticket or find phone numbers for regional support.
