This Node Is Deprecated — This version of the node has been replaced with a new and improved version. The old version is kept for backwards-compatibility, but for all new workflows we suggest to use the version linked below.
Go to Suggested Replacementk-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).
If the optional PMML inport is connected and contains preprocessing operations in the TransformationDictionary those are added to the learned model. The node can be configured as follows:


number of clusters
The number of clusters (cluster centers) to be created.
max number of iterations
The number of iterations after which the algorithm terminates, independent of the accuracy improvement of the cluster centers.

Input Ports

Input to clustering. All numerical values and only these are considered for clustering.
Optional PMML port object containing preprocessing operations.

Output Ports

The input data labeled with the cluster they are contained in.
PMML cluster model


Cluster View
Displays the cluster prototypes in a tree-like structure, with each node containing the coordinates of the cluster center.


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