meanshift-class

This class contains the model obtained by the MEANSHIFT method.

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

meanshift-class function

<dl>
<dt><code>cluster</code></dt>
<dd>A vector of integers indicating the cluster to which each point is allocated.</dd></dl>
<dt><code>value</code></dt>
<dd>A vector or matrix containing the location of the classified local maxima in the support.</dd>
<dt><code>data</code></dt>
<dd>The leaning set.</dd>
<dt><code>kernel</code></dt>
<dd>A string indicating the kernel associated with the kernel density estimate that the mean shift is optimizing over.</dd>
<dt><code>bandwidth</code></dt>
<dd>Used in the kernel density estimate for steepest ascent classification.</dd>
<dt><code>alpha</code></dt>
<dd>A scalar tuning parameter for normal kernels.</dd>
<dt><code>iterations</code></dt>
<dd>The number of iterations to perform mean shift.</dd>
<dt><code>epsilon</code></dt>
<dd>A scalar used to determine when to terminate the iteration of a individual query point.</dd>
<dt><code>epsilonCluster</code></dt>
<dd>A scalar used to determine the minimum distance between distinct clusters.</dd>

Slots

MeanShift model — meanshift-class

<dl>
<dt><code>cluster</code></dt>
<dd>A vector of integers indicating the cluster to which each point is allocated.</dd>


<dt><code>value</code></dt>
<dd>A vector or matrix containing the location of the classified local maxima in the support.</dd>


<dt><code>data</code></dt>
<dd>The leaning set.</dd>


<dt><code>kernel</code></dt>
<dd>A string indicating the kernel associated with the kernel density estimate that the mean shift is optimizing over.</dd>


<dt><code>bandwidth</code></dt>
<dd>Used in the kernel density estimate for steepest ascent classification.</dd>


<dt><code>alpha</code></dt>
<dd>A scalar tuning parameter for normal kernels.</dd>


<dt><code>iterations</code></dt>
<dd>The number of iterations to perform mean shift.</dd>


<dt><code>epsilon</code></dt>
<dd>A scalar used to determine when to terminate the iteration of a individual query point.</dd>


<dt><code>epsilonCluster</code></dt>
<dd>A scalar used to determine the minimum distance between distinct clusters.</dd>


</dl>

meanshift-class: MeanShift model

Description

Arguments

Slots

See Also