A package for efficient computations of standard clustering comparison measures. Available measures are described in the paper of Vinh et al, JMLR, 2009 (see reference below).
The functions included in aricode are:
ARI: computes the adjusted rand index
RI: computes the rand index
NVI: computes the normalized variation information
NID: computes the normalized information distance
NMI: computes the normalized mutual information
entropy: computes the conditional and joint entropies
clustComp: computes all clustering comparison measures at once
Traditional implementations (e.g., function adjustedRandIndex of package mclust) are in Omega(n + u v) where n is the size of the vectors the classifications of which are to be compared, u and v are the respective number of classes in each vectors. Here, the implementation is in Theta(n), plus the gain of speed due to the C code.
Nguyen Xuan Vinh, Julien Epps, and James Bailey. "Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance." Journal of Machine Learning Research 11.Oct (2010): 2837-2854. as described in Vinh et al (2009)