A novel parallelization approach for hierarchical clustering

Identification of groups of genes that manifest similar expression patters is a key step in the analysis of gene expression data. Hierarchical clustering is developed for that purpose. A fundamental problem with the previous implementations of this clustering method is its limitation to handle large data sets within a reasonable time and memory resources. In this paper, we present a parallel approach for solving this problem. Implementation of the parallel algorithm is illustrated on data from high dimensional microarray experiments related to the gene expression in cancerous disease and Arabidopsis seedling growth. They show considerable reduction in computational time and inter-node communication overhead, especially for large data sets.