Issue No. 05 - Sept.-Oct. (2014 vol. 11)
Danilo Horta , Instituto de Ciências Matemáticas e de Computação ¿ ICMC., Universidade de São Paulo ¿ Campus de São Carlos., Caixa Postal 668,, São Carlos-SP, Brazil
Ricardo J.G.B. Campello , Instituto de Ciências Matemáticas e de Computação ¿ ICMC., Universidade de São Paulo ¿ Campus de São Carlos., Caixa Postal 668,, São Carlos-SP, Brazil
The comparison of ordinary partitions of a set of objects is well established in the clustering literature, which comprehends several studies on the analysis of the properties of similarity measures for comparing partitions. However, similarity measures for clusterings are not readily applicable to biclusterings, since each bicluster is a tuple of two sets (of rows and columns), whereas a cluster is only a single set (of rows). Some biclustering similarity measures have been defined as minor contributions in papers which primarily report on proposals and evaluation of biclustering algorithms or comparative analyses of biclustering algorithms. The consequence is that some desirable properties of such measures have been overlooked in the literature. We review 14 biclustering similarity measures. We define eight desirable properties of a biclustering measure, discuss their importance, and prove which properties each of the reviewed measures has. We show examples drawn and inspired from important studies in which several biclustering measures convey misleading evaluations due to the absence of one or more of the discussed properties. We also advocate the use of a more general comparison approach that is based on the idea of transforming the original problem of comparing biclusterings into an equivalent problem of comparing clustering partitions with overlapping clusters.
Clustering algorithms, Bioinformatics, IEEE transactions, Computational biology, Algorithm design and analysis, Gene expression, Bismuth,validity index, Biclustering similarity measure, gene expression, external evaluation
Danilo Horta, Ricardo J.G.B. Campello, "Similarity Measures for Comparing Biclusterings", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 11, no. , pp. 942-954, Sept.-Oct. 2014, doi:10.1109/TCBB.2014.2325016