SoftClustering (version 2.1.3)

HardKMeans: Hard k-Means

Description

HardKMeans performs classic (hard) k-means.

Usage

HardKMeans(dataMatrix, meansMatrix, nClusters, maxIterations)

Value

$upperApprox: Obtained upper approximations [nObjects x nClusters]. Note: Apply function createLowerMShipMatrix() to obtain lower approximations; and for the boundary: boundary = upperApprox - lowerApprox.

$clusterMeans: Obtained means [nClusters x nFeatures].

$nIterations: Number of iterations.

Arguments

dataMatrix

Matrix with the objects to be clustered. Dimension: [nObjects x nFeatures].

meansMatrix

Select means derived from 1 = random (unity interval), 2 = maximum distances, matrix [nClusters x nFeatures] = self-defined means. Default: 2 = maximum distances.

nClusters

Number of clusters: Integer in [2, nObjects). Note, nCluster must be set even when meansMatrix is a matrix. For transparency, nClusters will not be overridden by the number of clusters derived from meansMatrix. Default: nClusters=2.

maxIterations

Maximum number of iterations. Default: maxIterations=100.

Author

M. Goetz, G. Peters, Y. Richter, D. Sacker, T. Wochinger.

References

Lloyd, S.P. (1982) Least squares quantization in PCM. IEEE Transactions on Information Theory 28, 128--137. <doi:10.1016/j.ijar.2012.10.003>.

Peters, G.; Crespo, F.; Lingras, P. and Weber, R. (2013) Soft clustering -- fuzzy and rough approaches and their extensions and derivatives. International Journal of Approximate Reasoning 54, 307--322. <doi:10.1016/j.ijar.2012.10.003>.

Examples

# An illustrative example clustering the sample data set DemoDataC2D2a.txt
HardKMeans(DemoDataC2D2a, 2, 2, 100)