A new cluster validity index for the fuzzy c-mean
Pattern Recognition Letters
Practical Handbook of Genetic Algorithms
Practical Handbook of Genetic Algorithms
An evolutionary technique based on K-means algorithm for optimal clustering in RN
Information Sciences—Applications: An International Journal
Fuzzy cluster validation index based on inter-cluster proximity
Pattern Recognition Letters
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolving connectionist systems for knowledge discovery from gene expression data of cancer tissue
Artificial Intelligence in Medicine
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Computers in Biology and Medicine
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Clustering analysis of the gene expression profiles has been used for identifying the functions of unknown genes. Fuzzy clustering method, which is one category of clustering, assigns one sample to multiple clusters as their degrees of membership. It is more appropriate for analyzing gene expression profiles because genes usually belong to multiple functional families. However, general clustering methods have problems that they are sensitive to initialization and can be trapped into local optima. In this paper, we propose an evolutionary fuzzy clustering method with Bayesian validation which uses a genetic algorithm for fuzzy clustering process of gene expression profiles and Bayesian validation method for the fitness evaluation process. We have conducted in-depth experiments to verify the usefulness of the proposed method with well-known gene expression profiles of SRBCT and Saccharomyces.