Abstract
ABSTRACTThe advancement in microarray technology has led to a flare-up of gene expression data, thus giving rise to need of efficient technique to analyze these massive data-sets. Among the existing techniques, clustering is a well-known technique used for analysis of microarray data. In this paper, a novel metaheuristic clustering approach for analysis of gene expression is proposed. A variance-based harmony search algorithm is used as an underlying metaheuristic algorithm. The performance of the proposed approach has been tested on 6 real-life data-sets and compared with 12 well-known clustering techniques. The experimental results reveal that the proposed technique outperforms the other existing techniques. The statistical and biological significance tests have also been carried out to demonstrate the superiority of the proposed technique.
Published Version
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