Bioinformatics Advance Access originally published online on May 5, 2007
Bioinformatics 2007 23(13):1607-1615; doi:10.1093/bioinformatics/btm158
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Weighted rank aggregation of cluster validation measures: a Monte Carlo cross-entropy approach
Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202, USA
*To whom correspondence should be addressed.
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Abstract |
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Motivation: Biologists often employ clustering techniques in the explorative phase of microarray data analysis to discover relevant biological groupings. Given the availability of numerous clustering algorithms in the machine-learning literature, an user might want to select one that performs the best for his/her data set or application. While various validation measures have been proposed over the years to judge the quality of clusters produced by a given clustering algorithm including their biological relevance, unfortunately, a given clustering algorithm can perform poorly under one validation measure while outperforming many other algorithms under another validation measure. A manual synthesis of results from multiple validation measures is nearly impossible in practice, especially, when a large number of clustering algorithms are to be compared using several measures. An automated and objective way of reconciling the rankings is needed.
Results: Using a Monte Carlo cross-entropy algorithm, we successfully combine the ranks of a set of clustering algorithms under consideration via a weighted aggregation that optimizes a distance criterion. The proposed weighted rank aggregation allows for a far more objective and automated assessment of clustering results than a simple visual inspection. We illustrate our procedure using one simulated as well as three real gene expression data sets from various platforms where we rank a total of eleven clustering algorithms using a combined examination of 10 different validation measures. The aggregate rankings were found for a given number of clusters k and also for an entire range of k.
Availability: R code for all validation measures and rank aggregation is available from the authors upon request.
Contact: somnath.datta{at}louisville.edu
Supplementary information: Supplementary information are available at http://www.somnathdatta.org/Supp/RankCluster/supp.htm.
Associate Editor: Joaquin Dopazo
Received on December 7, 2006; revised on April 9, 2007; accepted on April 18, 2007
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