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).

Options

Column selection

The numerical columns to be used for clustering.

Number of clusters

The number of clusters (cluster centers) to be created.

Centroid initialization

Determine how the initial centroids (cluster centers) are chosen.

• Random initialization: Initializes the centroids with random rows of the input table. Checking the Use static random seed it is possible to get reproducible results.
• First k rows: Initializes the centroids using the first rows of the input table.

Use static random seed

When checked, uses the provided static seed for reproducible random centroid initialization.

Seed value

The seed value for random centroid initialization.

New

Generate a new random seed value.

Max number of iterations

The maximum number of iterations after which the algorithm terminates if it hasn't found a stable solution before.

Enable Hilite Mapping

If enabled, the hiliting of a cluster row (2nd output) will hilite all rows of this cluster in the input table and the 1st output table. Depending on the number of rows, enabling this feature might consume a lot of memory.

Views

Cluster View

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

Workflows

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Links

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Developers