classificationErrorDistance

classificationErrorDistance,Partition,Partition-method

Compute the classification error distance
$$1 - \frac{1}{n} \max_{\sigma}{\sum_{C \in \cal{P}}{|C \cap \sigma(C)|}}$$
with $\sigma$ a weighted matching between the clusters of both partitions.
The nodes are the classes of each partition, the weights are the overlap of objects.

Provides several measures ((dis)similarity, distance/metric,
correlation, entropy) for comparing two partitions of the same set of
objects. The different measures can be assigned to three different
classes: Pair comparison (containing the famous Jaccard and Rand
indices), set based, and information theory based.
Many of the implemented measures can be found in
Albatineh AN, Niewiadomska-Bugaj M and Mihalko D (2006)
<doi:10.1007/s00357-006-0017-z> and
Meila M (2007) <doi:10.1016/j.jmva.2006.11.013>.
Partitions are represented by vectors of class labels which allow a
straightforward integration with existing clustering algorithms
(e.g. kmeans()). The package is mostly based on the S4 object system.

Fabian Ball

partitionComparison

Implements Measures for the Comparison of Two Partitions

Andreas Geyer-Schulz

classificationErrorDistance function

<dl><dt>p</dt>
<dd>The partition $P$</dd>
<dt>q</dt>
<dd>The partition $Q$</dd></dl>

Arguments

<ul>
<li><code>classificationErrorDistance(p = Partition, q = Partition)</code>: Compute given two partitions</li>
</ul>

Methods (by class)

This measure is implemented using <code>lp.assign</code> from
the <code>lpSolve</code> package to compute the maxmimal matching of a 
weighted bipartite graph.

Hint

Fabian Ball <a href="/link/fabian.ball%40kit.edu?package=partitionComparison&version=0.2.6" data-mini-rdoc="partitionComparison::fabian.ball@kit.edu">fabian.ball@kit.edu</a>

Author