HardKMeansDemo: Hard k-Means Demo

In SoftClustering: Soft Clustering Algorithms

Hard k-Means Demo

Description

HardKMeansDemo shows how hard k-means performs stepwise. The number of features is set to 2 and the maximum number of iterations is 100.

Usage

HardKMeansDemo(dataMatrix, meansMatrix, nClusters)

Arguments

dataMatrix	Matrix with the objects to be clustered. Dimension: [nObjects x nFeatures]. Default: no default set.
meansMatrix	Select means derived from 1 = random (unity interval), 2 = maximum distances, matrix [nClusters x nFeatures=2] = self-defined means. Default: meansMatrix=1 (random).
nClusters	Number of clusters: Integer in [2, min(5, nObjects-1)]. 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.

Value

None.

Author(s)

G. Peters.

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

# Clustering the data set DemoDataC2D2a.txt (nClusters=2, random initial means)
HardKMeansDemo(DemoDataC2D2a,1,2)
# Clustering the data set DemoDataC2D2a.txt (nClusters=2,3,4; initially set means)
HardKMeansDemo(DemoDataC2D2a,initMeansC2D2a,2)
HardKMeansDemo(DemoDataC2D2a,initMeansC3D2a,3)
HardKMeansDemo(DemoDataC2D2a,initMeansC4D2a,4)
# Clustering the data set DemoDataC2D2a.txt (nClusters=5, initially set means)
# It leads to an empty cluster: a (rare) case for an abnormal termination of k-means.
HardKMeansDemo(DemoDataC2D2a,initMeansC5D2a,5)

SoftClustering documentation built on Aug. 18, 2023, 9:08 a.m.