ClusterData_KMeans


Clusters data using KMeans algorithm.

Syntax

C++
C#
Python
 
def ClusterData_KMeans(
	inData: list[ list[float] ],
	inClusteringMethod: KMeansClusteringMethod,
	outCentroids: Matrix,
	/,
	*,
	inClusters: int = 2,
	inMaxIterations: int = 200,
	inSeed: int = 5489,
	inTerminationFactor: float = 1.5
)
-> (
	outPointToClusterAssignment: list[int],
	outDistanceSum: float
)

Parameters

Name Type Range Default Description
Input value inData list[ list[float] ] Data set, array of examples
Input value inClusters int 2 - + 2 Number of clusters to extract
Input value inMaxIterations int 10 - 1000 200 Maximal number of procedure iterations
Input value inSeed int 0 - 5489 Seed to init random engine
Input value inTerminationFactor float 1.0 - 2.0 1.5 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 list[int] Array of input point assignments to generated clusters
Output value outDistanceSum float Sum of squared distances from points to its respective cluster centroids