Clustering of gene expression profiles has been used for gene function identification. Since the genes usually belong to multiple functional families, fuzzy clustering methods are appropriate. However, a natural way to measure the quality of the fuzzy cluster partitions is still required. A Bayesian validation method for fuzzy partition selection with the largest posterior probability given the dataset is proposed. This method is compared to four representative fuzzy cluster validity measures using fuzzy c-means algorithm on four well-known datasets in terms of the number of clusters predicted in the data. An analysis of Saccharomyces cerevisiae cell cycle gene expression data follows to show the usefulness of the proposed method.