k-Means

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).

Input ports

1. Type: Table
   Clustering input
   Input to clustering. All numerical values and only these are considered for clustering.

Output ports

1. Type: Table
   Labeled input
   The input data labeled with the cluster they are contained in.
2. Type: Table
   Clusters
   The created clusters
3. Type: PMML
   PMML Cluster Model
   PMML cluster model