Evolution Strategy Applied to Global Optimization of Clusters in Gene Expression Data of DNA Microarrays

Cluster analysis is the most important method for analyzing large-scale gene expression patterns. The matrix representation of microarray data and its successive ‘optimal’ incisional hyperplanes that create topdown hierarchical tree are a useful platform for developing optimization algorithms to determine the ‘optimal’ clusters from a pairwise proximity matrix which represents completely connected and weighted graph [1]. Evolution strategy is applied to determine the ‘globally optimal’ incisional hyperplanes to construct hierarchical tree structure and tested with Fisher’s iris and Golub’s leukemia data sets. The results were compared with those of bottom-up hierarchical clustering, K-means and SOMs(Self-Organizing Maps) algorithms with promising results.