Efficient two dimensional clustering of microarray gene expression data by means of hybrid similarity measure

Microarrays are one of the most recent ameliorations in experimental molecular biology. Handling and analysis of microarray data is a most challenging task. The cluster analysis is one of the important high level analysis techniques, often exploited for microarray data analysis. As proteins usually related with different groups of proteins in order to handle diverse biological roles, the genes that create such proteins are thus expected to interact with more than one group of genes. This construes that in micro array gene expression data, a gene may make its presence in more than one cluster. The prior research has expressed the presence of genes in one or more clusters consistent with the nature of the gene and its attributes by the two dimensional clustering technique. The competence of the clustering analysis depends on the designing of an efficient (dis) similarity measure for grouping them. This research, has improved the prior cluster analysis research via an efficient hybrid distance based similarity measure. The proposed technique is implemented and its performance is evaluated with microarray gene expression data.