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SoftClustering (version 2.1.3)

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

172

Version

2.1.3

License

GPL-2

Maintainer

G. Peters

Last Published

August 18th, 2023

Functions in SoftClustering (2.1.3)

RoughKMeans_SHELL

Rough k-Means Shell
RoughKMeans_PE

Peters' Rough k-Means
datatypeInteger

Rough k-Means Plotting
HardKMeansDemo

Hard k-Means Demo
RoughKMeans_PI

PI Rough k-Means
DemoDataC2D2a

A small two-dimensional dataset with two clusters for demonstration purposes. See examples in the Help/Description of a function, e.g. for HardKMeansDemo().
HardKMeans

Hard k-Means
createLowerMShipMatrix

Create Lower Approximation
RoughKMeans_LW

Lingras & West's Rough k-Means
normalizeMatrix

Matrix Normalization
plotRoughKMeans

Rough k-Means Plotting
initializeMeansMatrix

Initialize Means Matrix
initMeansC3D2a

Two-dimensional dataset with three initial cluster means for the dataset DemoDataC2D2a. See examples in the Help/Description of a function, e.g. for HardKMeansDemo().
initMeansC2D2a

Two-dimensional dataset with two initial cluster means for the dataset DemoDataC2D2a. See examples in the Help/Description of a function, e.g. for HardKMeansDemo().
initMeansC5D2a

Two-dimensional dataset with five initial cluster means for the dataset DemoDataC2D2a. See examples in the Help/Description of a function, e.g. for HardKMeansDemo().
initMeansC4D2a

Two-dimensional dataset with four initial cluster means for the dataset DemoDataC2D2a. See examples in the Help/Description of a function, e.g. for HardKMeansDemo().