clustereval

Easy clustering evaluation in MATLAB.
Copyright (c) 2015 Taehoon Lee

Usage

Input arguments are two clustering results and metric name.
clustereval(a, b, 'metric name')

Example Code

X = rand(100, 2);
Z = linkage(X, 'average', 'euclidean');
a = cluster(Z, 'maxclust', 4);
b = kmeans(X, 4);
clustereval(a, b, 'ari') % adjusted Rand index

Implemented Metrics

• ri: the Rand Index
  • Rand, "Objective Criteria for the Evaluation of Clustering Methods", JASA, 1971.
• mi: the Mirkin index
• hi: the Hubert index
• ari: adjusted Rand index
  • Hubert and Arabie, "Comparing partitions", Journal of Classification, 1985.
• fowlkes: the Fowlkes-Mallows index
  • Fowlkes and Mallows, "A Method for Comparing Two Hierarchical Clustering", JASA, 1983.
• chi: Pearson's chi-square test
  • Chernoff and Lehmann, "The Use of Maximum Likelihood Estimates in \chi^2 Tests for Goodness of Fit", AMS, 1954.
• cramer: Cramer's coefficient
• tchouproff: Tchouproff's coefficient
• moc: the Measure of Concordance
  • Pfitzner et al., "Characterization and evaluation of similaritymeasures for pairs of clusterings", KIS, 2009.
• nmi: Normalized Mutual Index
  • Strehl and Ghosh, "Cluster ensembles - a knowledge reuse framework for combining multiple partitions", JMLR, 2002.