F-statistics algorithm for gene clustering evaluation

Authors:
Mohamad Qayoom;Qi Zhang;Christopher Taylor
Affiliations:
University of New Orleans, New Orleans, LA;University of New Orleans, New Orleans, LA;University of New Orleans, New Orleans, LA
Venue:
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Year:
2010

Citing 4
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Abstract

An enormous amount of microarray data has been generated and archived for a large variety of biological studies such as gene expression. In order to analyze gene expression data, many clustering algorithms have been proposed, but very few techniques have been developed to evaluate those clustering algorithms. A clustering evaluation method is used to find the degree of similarity between members of the same clusters and members of different clusters. We propose a new clustering evaluation technique F-Statistics Algorithm for Clustering Evaluation (FACE), which uses both inter-cluster and intracluster distances, and can be used to improve performance of clustering methods. We describe and evaluate FACE in the context of bioinformatics clustering by comparison with existing evaluation measurements on a set of yeast data. Results show that FACE is more stable and makes better conclusions.