initializeMeansMatrix

initializeMeansMatrix delivers an initial means matrix.

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

initializeMeansMatrix function

<dl><dt>dataMatrix</dt>
<dd>Matrix with the objects as basis for the means matrix.</dd>
<dt>nClusters</dt>
<dd>Number of clusters.</dd>
<dt>meansMatrix</dt>
<dd>Select means derived from 1 = random (unity interval), 2 = maximum distances, matrix [nClusters x nFeatures] = self-defined means (will be returned unchanged). Default: 2 = maximum distances.</dd></dl>

Arguments

M. Goetz, G. Peters, Y. Richter, D. Sacker, T. Wochinger.

Author

Initialize Means Matrix — initializeMeansMatrix

<dl>

<dt>dataMatrix</dt>
<dd>Matrix with the objects as basis for the means matrix.</dd>


<dt>nClusters</dt>
<dd>Number of clusters.</dd>


<dt>meansMatrix</dt>
<dd>Select means derived from 1 = random (unity interval), 2 = maximum distances, matrix [nClusters x nFeatures] = self-defined means (will be returned unchanged). Default: 2 = maximum distances.</dd>

</dl>

initializeMeansMatrix: Initialize Means Matrix

Description

Usage

Value

Arguments

Author