Optimized K-Means (Silhouette Coefficient)
This component determines the best number of clusters (k) for k-Means according to the mean silhouette coefficient. The component uses the Parameter Optimization Loop which retrains k-Means with a different k at each iteration. In the dialog, select the columns used for k-Means, set the range of tested k's by choosing a start value, the maximum number of iterations and the step size taken at each iteration. The data gets shuffled using the configured seed before passing it to k-Means to prevent bad initialization of the cluster centers in case the data is ordered. The clustering algorithm uses the Euclidean distance on the selected attributes. The data is not normalized by the node (if required, you should consider to use the "Normalizer" as a preprocessing step).
- Type: Data Table containing the data to be clustered.
- Type: Data Table containing the different k-values and the mean silhoutte coefficient sorted according the best silhouette coefficient.
- Type: Data The input data labeled with the cluster they are contained in. Output from k-Means for best k.
- Type: Data The created clusters from k-Means with best k.
- Type: PMML PMML cluster model from k-Means with best k.