SoftClustering (version 1.1502)

RoughKMeans_PE: Peters' Rough k-Means

Description

RoughKMeans_PE performs Peters' k-means clustering algorithm.

Usage

RoughKMeans_PE(dataMatrix, meansMatrix, nClusters, maxIterations, threshold, weightLower)

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.

threshold

Relative threshold in rough k-means algorithms (threshold >= 1.0). Default: threshold = 1.5.

weightLower

Weight of the lower approximation in rough k-means algorithms (0.0 <= weightLower <= 1.0). Default: weightLower = 0.7.

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.

References

Peters, G. (2006) Some refinements of rough k-means clustering. Pattern Recognition 39, 1481--1491.

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.

Peters, G. (2015) Is there any need for rough clustering? Pattern Recognition Letters 53, 31--37.

Examples

# NOT RUN {
# An illustrative example clustering the sample data set DemoDataC2D2a.txt
RoughKMeans_PE(DemoDataC2D2a, 2, 2, 100, 1.5, 0.7)
# }