A mixture model approach for the analysis of microarray gene expression data
Computational Statistics & Data Analysis
Generalized fuzzy c-means clustering strategies using Lp norm distances
IEEE Transactions on Fuzzy Systems
Self-splitting competitive learning: a new on-line clustering paradigm
IEEE Transactions on Neural Networks
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As the gene sequencing technology has been maturating, genomes of more and more organisms and genetic sequences are available in international repositories. However, the biological functions of most of these genes remain unknown. Microarray technology opens a door to discover information of underlying genes and is widely used in basic research and target discovery, biomarker determination, pharmacology, target selectivity, development of prognostic tests and disease-subclass determination. Clustering is one of the typical methods of analyzing microarray data. By clustering the gene microarray expression data into categories with similar profiles, genes with similar function can be focused on. There are many clustering methods used for the analysis of gene microarray data. However, they usually suffer from some shortcomings, such as sensitive to initial input, inappropriate grouping, difficult to discover natural or near optimal clusters, and so on. In this paper, we propose a novel clustering method to discover the optimal clusters by searching PPVs (Pair of Prototype Vector). The experiment results show that our method works very well.