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SoftClustering (version 1.1502)

Soft Clustering Algorithms

Description

It contains soft clustering algorithms, in particular approaches derived from rough set theory: Lingras & West original rough k-means, Peters' refined rough k-means, and PI rough k-means. It also contains classic k-means and a corresponding illustrative demo.

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Version

Install

install.packages('SoftClustering')

Monthly Downloads

180

Version

1.1502

License

GPL-2

Maintainer

G Peters

Last Published

February 10th, 2015

Functions in SoftClustering (1.1502)

HardKMeansDemo

Hard k-Means Demo
createLowerMShipMatrix

Create Lower Approximation
RoughKMeans_PI

PI Rough k-Means
RoughKMeans_PE

Peters' Rough k-Means
initMeansC3D2a

Initial Means (3 Clusters)
normalizeMatrix

Matrix Normalization
initMeansC4D2a

Initial Means (4 Clusters)
RoughKMeans_SHELL

Rough k-Means Shell
initMeansC2D2a

Initial Means (2 Clusters)
DemoDataC2D2a

A Simple Data Set
datatypeInteger

Rough k-Means Plotting
RoughKMeans_LW

Lingras & West's Rough k-Means
initMeansC5D2a

Initial Means (5 Clusters)
plotRoughKMeans

Rough k-Means Plotting
HardKMeans

Hard k-Means
initializeMeansMatrix

Initialize Means Matrix