clusterFunSel

A function that generates formatted algorithmic functions that can be plugged
to enable run a wide variety of clustering algorithm for `clusGapDiscr` function.

The gap statistic approach is extended to estimate the number of clusters for categorical response format data. This approach and accompanying software is designed to be used with the output of any clustering algorithm and with distances specifically designed for categorical (i.e. multiple choice) or ordinal survey response data.

Eduardo Cortes

DiscreteGapStatistic

An Extension of the Gap Statistic for Ordinal/Categorical Data

Jeffrey Miecznikowski

clusterFunSel function

<dl><dt>clustFun</dt>
<dd>A character string with the following possible options:
'pam' (default) from `cluster::pam`, 'diana' from `cluster::diana`, 'fanny' from `cluster::fanny`,
'agnes-{average, single, complete, ward, weighted}' from `cluster::agnes`,
'hclust-{ward.D, ward.D2, single, complete, average, mcquitty, median, centroid}' from `base::hclust`,
'kmodes' from `klar::kmodes` (`iter.max = 10`, `weighted = FALSE` and `fast= TRUE`).
'kmodes-N' enables to run the `kmodes` algorithm with a given number N of iterations where `iter.max = N`.</dd></dl>

Arguments

Clustering generating function — clusterFunSel

<dl>

<dt>clustFun</dt>
<dd>A character string with the following possible options:
'pam' (default) from `cluster::pam`, 'diana' from `cluster::diana`, 'fanny' from `cluster::fanny`,
'agnes-{average, single, complete, ward, weighted}' from `cluster::agnes`,
'hclust-{ward.D, ward.D2, single, complete, average, mcquitty, median, centroid}' from `base::hclust`,
'kmodes' from `klar::kmodes` (`iter.max = 10`, `weighted = FALSE` and `fast= TRUE`).
'kmodes-N' enables to run the `kmodes` algorithm with a given number N of iterations where `iter.max = N`.</dd>

</dl>

clusterFunSel: Clustering generating function

Description

Usage

Value

Arguments