CLUSTER ANALYSIS OF MICROARRAY DATA BASED ON SIMILARITY MEASUREMENT

DNA microarray technology is a fundamental tool in gene expression data analysis. The collection of datasets from the technology has underscored the need for quantitative analytical tools to examine such data. Due to the large number of genes and complex gene regulation networks, clustering is a useful exploratory technique for analyzing these data. Many clustering algorithms have been proposed to analyze microarray gene expression data, but very few of them evaluate the quality of the clusters. In this paper, a novel cluster analysis technique has been proposed without considering number of clusters a priori. The method computes a similarity measurement function based on which the clusters are merged and subsequently splits a cluster by computing the degree of separation of the cluster. The process of splitting and merging performs iteratively until the cluster validity index (i.e. DB index) degrades. The experimental result shows that the proposed cluster analysis technique gives comparable results on gene cancer dataset with existing methods. This study may help raise relevant issues in the extraction of meaningful biological information from microarray expression data.