compare.accuracy

Comparison of two sets of clusters, using accuracy

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

compare.accuracy function

<dl><dt>clus</dt>
<dd>The extracted clusters.</dd>
<dt>gt</dt>
<dd>The real clusters.</dd>
<dt>comp</dt>
<dd>How the two partitions are compared. <code>"max"</code> matches each extracted
cluster with the class it agrees with most and averages the per-cluster scores, weighted by
cluster size; <code>"cluster"</code> returns those scores instead of averaging them;
<code>"pairwise"</code> ignores the labels and looks at every pair of observations, asking whether
the two partitions agree on grouping it or separating it.
In <code>"pairwise"</code> mode this function is the Rand index: the proportion of pairs
the two partitions agree on, counting both those they group and those they keep apart. See
<code>compare.jaccard</code> and <code>compare.kappa</code> for the two other readings
of the same pair counts.</dd></dl>

Arguments

Comparison of two sets of clusters, using accuracy — compare.accuracy

<dl>

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


<dt>gt</dt>
<dd>The real clusters.</dd>


<dt>comp</dt>
<dd>How the two partitions are compared. <code>"max"</code> matches each extracted
cluster with the class it agrees with most and averages the per-cluster scores, weighted by
cluster size; <code>"cluster"</code> returns those scores instead of averaging them;
<code>"pairwise"</code> ignores the labels and looks at every pair of observations, asking whether
the two partitions agree on grouping it or separating it.
In <code>"pairwise"</code> mode this function is the Rand index: the proportion of pairs
the two partitions agree on, counting both those they group and those they keep apart. See
<code>compare.jaccard</code> and <code>compare.kappa</code> for the two other readings
of the same pair counts.</dd>

</dl>

compare.accuracy: Comparison of two sets of clusters, using accuracy

Description

Usage

Value

Arguments

See Also

Examples