ClusterInit

ClusterInit-class

This class encapsulates the parameters of clustering initialization methods.

Algorithms and methods for model-based clustering and classification.
It supports various types of data: continuous, categorical and counting
and can handle mixed data of these types. It can fit Gaussian (with diagonal
covariance structure), gamma, categorical and Poisson models. The algorithms
also support missing values.

Serge Iovleff

MixAll

Clustering and Classification using Model-Based Mixture Models

Parmeet Bathia

ClusterInit function

<dl>
<dt><code>method</code></dt>
<dd>Character string with the initialization method to use. Default value: "class"</dd></dl>
<dt><code>nbInit</code></dt>
<dd>Integer defining the number of initialization to perform. Default value: 5.</dd>
<dt><code>algo</code></dt>
<dd>An instance of <code>ClusterAlgo</code> class.
Default value: <code>clusterAlgo("EM", 20, 0.01)</code>.</dd>

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Constructor of the [ClusterInit] class — ClusterInit

<dl>
<dt><code>method</code></dt>
<dd>Character string with the initialization method to use. Default value: "class"</dd>


<dt><code>nbInit</code></dt>
<dd>Integer defining the number of initialization to perform. Default value: 5.</dd>


<dt><code>algo</code></dt>
<dd>An instance of <code>ClusterAlgo</code> class.
Default value: <code>clusterAlgo("EM", 20, 0.01)</code>.</dd>


</dl>

ClusterInit: Constructor of the [`ClusterInit`] class

Description

Arguments

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Examples