LearnAlgo

LearnAlgo-class

This class encapsulates the parameters of clustering estimation algorithms
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

LearnAlgo function

<dl>
<dt><code>algo</code></dt>
<dd>A character string with the algorithm.
Possible values: "Simul", "Impute. Default value: "Simul".</dd></dl>
<dt><code>nbIteration</code></dt>
<dd>Integer defining the maximal number of iterations. Default value: 200.</dd>
<dt><code>epsilon</code></dt>
<dd>real defining the epsilon value for the algorithm. epsilon is
note used if <code>algo</code> is "Simul". Default value: 1e-07.</dd>

Slots

[LearnAlgo] class for Cluster algorithms. — LearnAlgo

<dl>
<dt><code>algo</code></dt>
<dd>A character string with the algorithm.
Possible values: "Simul", "Impute. Default value: "Simul".</dd>


<dt><code>nbIteration</code></dt>
<dd>Integer defining the maximal number of iterations. Default value: 200.</dd>


<dt><code>epsilon</code></dt>
<dd>real defining the epsilon value for the algorithm. epsilon is
note used if <code>algo</code> is "Simul". Default value: 1e-07.</dd>


</dl>

LearnAlgo: [`LearnAlgo`] class for Cluster algorithms.

Description

Arguments

Slots

Examples