clucenters

Determine cluster centers using the uniform distribution, taking into account
the number of clusters (<code>num_clusters</code>) and the average cluster separation
(<code>clu_sep</code>).
More specifically, let c=<code>num_clusters</code>,
s=s=<code>clu_sep</code>, o=o=<code>clu_offset</code>,
n=<code>length(clu_sep)</code> (i.e., number of dimensions). Cluster centers
are obtained according to the following equation:
C=cUdiag(s) +
1\,o^TC=cU.diag(s) + 1o'
where CC is the c nc x n matrix of
cluster centers, UU is an c nc x n matrix
of random values drawn from the uniform distribution between -0.5 and 0.5,
and 11 is an c 1c x 1 vector with all
entries equal to 1.

An implementation of the clugen algorithm for generating
multidimensional clusters with arbitrary distributions. Each cluster is
supported by a line segment, the position, orientation and length of which
guide where the respective points are placed. This package is described in
Fachada & de Andrade (2023) <doi:10.1016/j.knosys.2023.110836>.

Nuno Fachada

clugenr

Multidimensional Cluster Generation Using Support Lines

clucenters function

<dl><dt>num_clusters</dt>
<dd>Number of clusters.</dd>
<dt>clu_sep</dt>
<dd>Average cluster separation (n 1n x 1 vector).</dd>
<dt>clu_offset</dt>
<dd>Cluster offsets (n 1n x 1 vector).</dd></dl>

Arguments

Determine cluster centers using the uniform distribution — clucenters

<dl>

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


<dt>clu_sep</dt>
<dd>Average cluster separation (n 1n x 1 vector).</dd>


<dt>clu_offset</dt>
<dd>Cluster offsets (n 1n x 1 vector).</dd>

</dl>

clucenters: Determine cluster centers using the uniform distribution

Description

Usage

Value

Arguments

Examples