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ClusterData_KMeans

Clusters data using KMeans algorithm.

	Name	Type	Range	Description
	inData	Real Array Array		Data set, array of examples
	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 in Error Handling.

List of possible exceptions:

Error type	Description
DomainError	Cannot make more clusters than there is data in input dataset in ClusterData_KMeans.
DomainError	Empty dataset on input in ClusterData_KMeans.
DomainError	Inconsistent number of data coordinates in input dataset in ClusterData_KMeans.

Complexity Level

This filter is available on Expert Complexity Level.