classificationErrorDistance: Classification Error Distance

In partitionComparison: Implements Measures for the Comparison of Two Partitions

Classification Error Distance

Description

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.

Usage

classificationErrorDistance(p, q)

## S4 method for signature 'Partition,Partition'
classificationErrorDistance(p, q)

Arguments

p	The partition P
q	The partition Q

Methods (by class)

• classificationErrorDistance(p = Partition, q = Partition): Compute given two partitions

Hint

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

Author(s)

Fabian Ball [e-mail]

References

\insertRef

Meila2001partitionComparison

\insertRef

Meila2005partitionComparison

Examples

isTRUE(all.equal(classificationErrorDistance(new("Partition", c(0, 0, 0, 1, 1)), 
                                             new("Partition", c(0, 0, 1, 1, 1))), 0.2))

partitionComparison documentation built on Aug. 24, 2023, 1:06 a.m.