plotRoughKMeans

plotRoughKMeans plots the rough clustering results in 2D. Note: Plotting is limited to a maximum of 5 clusters.

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.

G. Peters

SoftClustering

Soft Clustering Algorithms

plotRoughKMeans function

<dl><dt>dataMatrix</dt>
<dd>Matrix with the objects to be plotted.</dd>
<dt>upperMShipMatrix</dt>
<dd>Corresponding matrix with upper approximations.</dd>
<dt>meansMatrix</dt>
<dd>Corresponding means matrix.</dd>
<dt>plotDimensions</dt>
<dd>An integer vector of the length 2. Defines the to be plotted feature dimensions, i.e., max(plotDimensions = c(1:2)) &lt;= nFeatures. Default: plotDimensions = c(1:2).</dd>
<dt>colouredPlot</dt>
<dd>Select TRUE = colouredPlot plot, FALSE = black/white plot.</dd></dl>

Arguments

Author

Rough k-Means Plotting — plotRoughKMeans

<dl>

<dt>dataMatrix</dt>
<dd>Matrix with the objects to be plotted.</dd>


<dt>upperMShipMatrix</dt>
<dd>Corresponding matrix with upper approximations.</dd>


<dt>meansMatrix</dt>
<dd>Corresponding means matrix.</dd>


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


<dt>colouredPlot</dt>
<dd>Select TRUE = colouredPlot plot, FALSE = black/white plot.</dd>

</dl>

State of Data and AI Literacy Report 2025

plotRoughKMeans: Rough k-Means Plotting

Description

Usage

Value

Arguments

Author