CLUSTERING TECHNIQUES FOR DNA MICROARRAY BASED GENE EXPRESSION DATA SURVEY

The advent of DNA microarray technology has enabled biologists to monitor the expression levels (MRNA) of thousands of genes simultaneously. Cluster analysis seeks to partition a given data set into groups based on specified featuresso that the data points within a group are more similar to each other than the points in differentgroups. Many conventional clustering algorithms have been adapted or directly applied to gen e expression data, and also new algorithms have recently been proposed specifically aiming at gene expression data. These clustering algorithms have been proven useful for identifying biologicallyrelevant groups of genes and samples.In this survey, the var ious approaches to gene expression data analysis using clustering techniques are addressed. Then the performances of various existing clustering algorithms under each of these approachesare discussed. The specific challenges pertinent toeach clustering cat egory and introduce several representative approaches are presented. And also theproblems of cluster validation are discussed and review various methods to assess the qualityand reliability of clustering results. Finally, this paper is concluded and sugges ts the promisingtrends in this field