SoftClustering (version 1.1502)

RoughKMeans_SHELL: Rough k-Means Shell

Description

RoughKMeans_SHELL performs rough k-means algorithms with options for normalization and a 2D-plot of the results.

Usage

RoughKMeans_SHELL(clusterAlgorithm, dataMatrix, meansMatrix, nClusters,
                  normalizationMethod, maxIterations, plotDimensions,
                  colouredPlot, threshold, weightLower)

Arguments

clusterAlgorithm

Select 0 = classic k-means, 1 = Lingras & West's rough k-means, 2 = Peters' rough k-means, 3 = \(\pi\) rough k-means. Default: clusterAlgorithm = 3 (\(\pi\) rough k-means).

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. Note: Plotting is limited to a maximum of 5 clusters.

normalizationMethod

1 = unity interval, 2 = normal distribution (sample variance), 3 = normal distribution (population variance). Any other value returns the matrix unchanged. Default: meansMatrix = 1 (unity interval).

maxIterations

Maximum number of iterations. Default: maxIterations=100.

plotDimensions

An integer vector of the length 2. Defines the to be plotted feature dimensions, i.e., max(plotDimensions = c(1:2)) <= nFeatures. Default: plotDimensions = c(1:2).

colouredPlot

Select TRUE = colouredPlot plot, FALSE = black/white plot.

threshold

Relative threshold in rough k-means algorithms (threshold >= 1.0). Default: threshold = 1.5. Note: It can be ignored for classic k-means.

weightLower

Weight of the lower approximation in rough k-means algorithms (0.0 <= weightLower <= 1.0). Default: weightLower = 0.7. Note: It can be ignored for classic k-means and \(\pi\) rough k-means

Value

2D-plot of clustering results. The boundary objects are represented by stars (*).

$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.

Examples

# NOT RUN {
# An illustrative example clustering the sample data set DemoDataC2D2a.txt
RoughKMeans_SHELL(3, DemoDataC2D2a, 2, 2, 1, 100, c(1:2), TRUE, 1.5, 0.7)
# }