intern.dunn

Evaluation a clustering algorithm according to Dunn's index.

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

intern.dunn function

<dl><dt>clus</dt>
<dd>The extracted clusters.</dd>
<dt>d</dt>
<dd>The dataset.</dd>
<dt>type</dt>
<dd>Indicates whether a "global" or a "cluster"-wise evaluation should be used. The
per-cluster values are the terms the global index is the minimum of: each cluster's distance
to the nearest other one, over the largest diameter of the partition.</dd></dl>

Arguments

Clustering evaluation through Dunn's index — intern.dunn

<dl>

<dt>clus</dt>
<dd>The extracted clusters.</dd>


<dt>d</dt>
<dd>The dataset.</dd>


<dt>type</dt>
<dd>Indicates whether a "global" or a "cluster"-wise evaluation should be used. The
per-cluster values are the terms the global index is the minimum of: each cluster's distance
to the nearest other one, over the largest diameter of the partition.</dd>

</dl>

intern.dunn: Clustering evaluation through Dunn's index

Description

Usage

Value

Arguments

See Also

Examples