normalizedMutualInformation

normalizedMutualInformation,Partition,Partition,character-method

normalizedMutualInformation,Partition,Partition,missing-method

One of "min" (default), "max" or "sum"

type

Compute the mutual information ($MI$) which is normalized either by the
minimum/maximum partition entropy ($H$)
$$\frac{MI(P, Q)}{\varphi(H(P), H(Q))},\ \varphi \in \{\min, \max\}$$
or the sum
$$\frac{2 \cdot MI(P, Q)}{H(P) + H(Q)}$$

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

normalizedMutualInformation function

<dl><dt>p</dt>
<dd>The partition $P$</dd>
<dt>q</dt>
<dd>The partition $Q$</dd>
<dt>type</dt>
<dd>One of "min" (default), "max" or "sum"</dd></dl>

Arguments

<ul>
<li><code>p = Partition,q = Partition,type = character</code>: Compute given two partitions</li>
<li><code>p = Partition,q = Partition,type = missing</code>: Compute given two partitions with <code>type="min"</code></li>
</ul>

Methods (by class)

Fabian Ball <a href="/link/fabian.ball%40kit.edu?package=partitionComparison&version=0.2.5" data-mini-rdoc="partitionComparison::fabian.ball@kit.edu">fabian.ball@kit.edu</a>

Author