This node outputs the cluster centers for a predefined number of clusters (no dynamic number of clusters). K-means performs a crisp clustering that assigns a data vector to exactly one cluster. The algorithm terminates when the cluster assignments do not change anymore.
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: TableClustering inputInput to clustering. All numerical values and only these are considered for clustering.