Partitions the data into <code>k</code> clusters around medoids -- actual observations of
the dataset -- rather than around means. Being an observation, a medoid can be shown to
students as a representative example of its cluster, and the method tolerates outliers much
better than K-means, which drags a mean towards them.

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

PAM function

<dl><dt>d</dt>
<dd>The dataset (<code>matrix</code> or <code>data.frame</code>).</dd>
<dt>k</dt>
<dd>The number of clusters.</dd>
<dt>criterion</dt>
<dd>How <code>k</code> is chosen, as in <code>KMEANS</code>. With <code>"none"</code> (the
default) <code>k</code> is used as it is; with <code>"silhouette"</code> it is chosen between 2 and
<code>k</code> by the mean silhouette width, the criterion PAM itself optimises the closest.</dd>
<dt>graph</dt>
<dd>A logical indicating whether the criterion curve is plotted.</dd>
<dt>seed</dt>
<dd>A specified seed for random number generation. PAM's initialisation is
deterministic, so this only matters for the criterion search.</dd>
<dt>...</dt>
<dd>Other parameters, passed to <code><a href="/link/pam?package=fdm2id&version=1.0.1" data-mini-rdoc="fdm2id::pam">pam</a></code>.</dd></dl>

Arguments

Clustering using K-medoids (PAM) — PAM

<dl>

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


<dt>k</dt>
<dd>The number of clusters.</dd>


<dt>criterion</dt>
<dd>How <code>k</code> is chosen, as in <code>KMEANS</code>. With <code>"none"</code> (the
default) <code>k</code> is used as it is; with <code>"silhouette"</code> it is chosen between 2 and
<code>k</code> by the mean silhouette width, the criterion PAM itself optimises the closest.</dd>


<dt>graph</dt>
<dd>A logical indicating whether the criterion curve is plotted.</dd>


<dt>seed</dt>
<dd>A specified seed for random number generation. PAM's initialisation is
deterministic, so this only matters for the criterion search.</dd>


<dt>...</dt>
<dd>Other parameters, passed to <code><a href='https://rdrr.io/pkg/cluster/man/pam.html'>pam</a></code>.</dd>

</dl>

PAM: Clustering using K-medoids (PAM)

Description

Usage

Value

Arguments

See Also

Examples