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A modified QT-clustering algorithm over Gene Expression data

Clustering is often one of the first steps in Gene Expression Analysis. In this paper we propose a modified-QT clustering algorithm for gene expression datasets that uses a modified Pearson's correlation measure to identify the clusters in gene expression data. Experimental results show the efficiency of the proposed method over several real-life datasets. The proposed method has been found to be better than other comparable algorithms in terms of z-score and p-value measures of cluster quality.