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Highly Coherent Pattern Identification Using Graph-based Clustering

This paper presents an enhanced graph based parameter independent clustering technique. The algorithm produces highly coherent clusters in terms of visual representation and cluster validity measures. The technique finds highly coherent patterns of genes having high biological relevance. The method was tested on four real life datasets and the results compared with those of other similar algorithms in terms of various quality measures and found to be significantly better.