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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.