Run the EM algorithm 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

EM function

<dl><dt>d</dt>
<dd>The dataset (<code>matrix</code> or <code>data.frame</code>).</dd>
<dt>k</dt>
<dd>Either an integer (the number of clusters) or a (<code>vector</code>) indicating the cluster to which each point is initially allocated.</dd>
<dt>model</dt>
<dd>A character string indicating the model. The help file for <code>mclustModelNames</code> describes the available models.</dd>
<dt>seed</dt>
<dd>A specified seed for random number generation (used only for the default k-means initialization).</dd>
<dt>...</dt>
<dd>Other parameters.</dd></dl>

Arguments

Expectation-Maximization clustering method — EM

<dl>

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


<dt>k</dt>
<dd>Either an integer (the number of clusters) or a (<code>vector</code>) indicating the cluster to which each point is initially allocated.</dd>


<dt>model</dt>
<dd>A character string indicating the model. The help file for <code>mclustModelNames</code> describes the available models.</dd>


<dt>seed</dt>
<dd>A specified seed for random number generation (used only for the default k-means initialization).</dd>


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

</dl>

EM: Expectation-Maximization clustering method

Description

Usage

Value

Arguments

See Also

Examples