Bayesian validation of fuzzy clustering for analysis of yeast cell cycle data

Abstract

Clustering for the analysis of the gene expression profiles has been used for identifying the functions of the genes and of unknown genes. Since the genes usually belong to multiple functional families, fuzzy clustering methods are more appropriate than the conventional hard clustering methods. However, it is still required to devise natural way to measure the quality of the cluster partitions that are obtained by fuzzy clustering. In this paper, a Bayesian validation method of selecting a fuzzy partition with the largest posterior probability given the dataset is proposed to evaluate the fuzzy partitions effectively. Analysis of yeast cell-cycle data follows to show the usefulness of the proposed method. © Springer-Verlag Berlin Heidelberg 2005.