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BMCBI
2007

Evaluation of gene-expression clustering via mutual information distance measure

8 years 11 months ago
Evaluation of gene-expression clustering via mutual information distance measure
Background: The definition of a distance measure plays a key role in the evaluation of different clustering solutions of gene expression profiles. In this empirical study we compare different clustering solutions when using the Mutual Information (MI) measure versus the use of the well known Euclidean distance and Pearson correlation coefficient. Results: Relying on several public gene expression datasets, we evaluate the homogeneity and separation scores of different clustering solutions. It was found that the use of the MI measure yields a more significant differentiation among erroneous clustering solutions. The proposed measure was also used to analyze the performance of several known clustering algorithms. A comparative study of these algorithms reveals that their "best solutions" are ranked almost oppositely when using different distance measures, despite the found correspondence between these measures when analysing the averaged scores of groups of solutions. Conclusi...
Ido Priness, Oded Maimon, Irad E. Ben-Gal
Added 08 Dec 2010
Updated 08 Dec 2010
Type Journal
Year 2007
Where BMCBI
Authors Ido Priness, Oded Maimon, Irad E. Ben-Gal
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