Run the HCA method for clustering.

Contains functions to simplify the use of data mining methods (classification, regression, clustering, etc.), for students and beginners in R programming. Various R packages are used and wrappers are built around the main functions, to standardize the use of data mining methods (input/output): it brings a certain loss of flexibility, but also a gain of simplicity. The package name came from the French "Fouille de Données en Master 2 Informatique Décisionnelle".

Alexandre Blansch<c3><a9>

fdm2id

Data Mining and R Programming for Beginners

Alexandre Blansché

HCA function

<dl><dt>d</dt>
<dd>The dataset (<code>matrix</code> or <code>data.frame</code>).</dd>
<dt>k</dt>
<dd>The number of cluster. If <code>NULL</code> (the default), it is set to the largest drop
in aggregation height.</dd>
<dt>method</dt>
<dd>Character string defining the clustering method.</dd>
<dt>engine</dt>
<dd>Which implementation builds the hierarchy: <code><a href="/link/hclust?package=fdm2id&version=1.0.1" data-mini-rdoc="fdm2id::hclust">hclust</a></code> (the
default) or <code><a href="/link/agnes?package=fdm2id&version=1.0.1" data-mini-rdoc="fdm2id::agnes">agnes</a></code>. They give the same hierarchy -- same heights, to
machine precision, and the same cut -- but <code>hclust</code> is far faster on a large dataset
(0.3 s against 30 s on 3000 observations). Use <code>"agnes"</code> for the linkages it alone
provides, or to compare the two.</dd>
<dt>graph</dt>
<dd>A logical indicating whether or not a graphic should be plotted (the
aggregation heights used to choose <code>k</code>, when <code>k</code> is not given).</dd>
<dt>...</dt>
<dd>Other parameters.</dd></dl>

Arguments

Hierarchical Cluster Analysis method — HCA

<dl>

<dt>d</dt>
<dd>The dataset (<code>matrix</code> or <code>data.frame</code>).</dd>


<dt>k</dt>
<dd>The number of cluster. If <code>NULL</code> (the default), it is set to the largest drop
in aggregation height.</dd>


<dt>method</dt>
<dd>Character string defining the clustering method.</dd>


<dt>engine</dt>
<dd>Which implementation builds the hierarchy: <code><a href='https://rdrr.io/r/stats/hclust.html'>hclust</a></code> (the
default) or <code><a href='https://rdrr.io/pkg/cluster/man/agnes.html'>agnes</a></code>. They give the same hierarchy -- same heights, to
machine precision, and the same cut -- but <code>hclust</code> is far faster on a large dataset
(0.3 s against 30 s on 3000 observations). Use <code>"agnes"</code> for the linkages it alone
provides, or to compare the two.</dd>


<dt>graph</dt>
<dd>A logical indicating whether or not a graphic should be plotted (the
aggregation heights used to choose <code>k</code>, when <code>k</code> is not given).</dd>


<dt>...</dt>
<dd>Other parameters.</dd>

</dl>

HCA: Hierarchical Cluster Analysis method

Description

Usage

Value

Arguments

See Also

Examples