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Analysis of Saccharomyces Cell Cycle Expression Data using Bayesian Validation of Fuzzy Clustering  

Yoo Si-Ho (연세대학교 컴퓨터과학과)
Won Hong-Hee (연세대학교 컴퓨터과학과)
Cho Sung-Bae (연세대학교 컴퓨터과학과)
Publication Information
Journal of KIISE:Software and Applications / v.31, no.12, 2004 , pp. 1591-1601 More about this Journal
Abstract
Clustering, a technique for the analysis of the genes, organizes the patterns into groups by the similarity of the dataset and has been used for identifying the functions of the genes in the cluster or analyzing the functions of unknown gones. Since the genes usually belong to multiple functional families, fuzzy clustering methods are more appropriate than the conventional hard clustering methods which assign a sample to a group. In this paper, a Bayesian validation method is proposed to evaluate the fuzzy partitions effectively. Bayesian validation method is a probability-based approach, selecting a fuzzy partition with the largest posterior probability given the dataset. At first, the proposed Bayesian validation method is compared to the 4 representative conventional fuzzy cluster validity measures in 4 well-known datasets where foray c-means algorithm is used. Then, we have analyzed the results of Saccharomyces cell cycle expression data evaluated by the proposed method.
Keywords
fuzzy clustering; fuzzy c-means algorithm; Bayesian validation method; Saccharomyces cell cycle expression data;
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