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ClusterData_KMeans

Expert

Module: FoundationPro

Clusters data using KMeans algorithm.

Name Type Range Description
Input value
 inData RealArrayArray Data set, array of examples
Input value
 inClusters Integer 2 - + Number of clusters to extract
Input value
 inMaxIterations Integer 10 - 1000 Maximal number of procedure iterations
Input value
 inSeed Integer 0 - Seed to init random engine
Input value
 inTerminationFactor Real 1.0 - 2.0 Additional factor of procedure stop
Input value
 inClusteringMethod KMeansClusteringMethod KMeans variant to use
Output value
 outCentroids Matrix Resulting centroid points in feature space
Output value
 outPointToClusterAssignment IntegerArray Array of input point assignments to generated clusters
Output value
 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.