Microarray Gene Expression Data Mining : Clustering Analysis Review

After genome sequencing, DNA microarray analysis has become the most widely used functional genomics approach in the bioinformatics field. Biologists are vastly plagued by the enormous amount of unprecedented qualities of genome-wide data produced by the DNA Microarray experiment. Clustering is the process of grouping data objects into set of disjoint classes called clusters so that objects within a class are highly similar with one another and dissimilar with the objects in other classes. It is presently the far most used method for gene expression analysis which provides a divide-and–conquer strategy to extract meaningful information from expression profile. This paper presents a review on the recently development of microarray clustering techniques. In this paper, the procedures of clustering analysis are highlighted followed by the different categories of gene expression data clustering with some conventional approaches, to provide a framework for an enhanced general understanding of related methods for further development.

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