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).
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:

Options

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.

Views

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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