Back to Adaptive Vision Studio website

You are here: Start » Filter Reference » Clustering » KMeansClustering

KMeansClustering

Clusters data using KMeans algorithm

	Name	Type	Range	Description
	inData	Matrix		Data set, where variables are in columns and examples are in rows.
	inClusters	Integer	2 - +	Number of clusters to extract.
	inMaxIterations	Integer	10 - 1000	Maximal number of procedure iterations
	inSeed	Integer	0 -	Seed to init random engine.
	inTerminationFactor	Real	1.0 - 2.0	Additional factor of procedure stop.
	inClusteringMethod	KMeansClusteringMethod		KMeans variant to use.
	outCentroids	Matrix		Resulting centroid points in feature space.
	outPointToClusterAssignment	Integer Array		Array of input point assignments to generated clusters.
	outDistanceSum	Real		Sum of squared distances from points to its respective cluster centroids.

Errors

This filter can throw an exception to report error. Read how to deal with errors here: Error Handling

Error type	Description
DomainError	Empty dataset on input in KMeansClustering.
DomainError	Cannot make more clusters than there is data in input dataset.

Complexity Level

This filter is available on Expert Complexity Level.