Harmony Search PSO Clustering for Tumor and Cancer Gene Expression Dataset

Enormous quantity of gene expression data from diverse data sources are accumulated due to the modern advancement in microarray technology that leads to major computational challenges. The foremost step towards addressing this challenge is to cluster genes which reveal hidden gene expression patterns and natural structures to find the interesting patterns from the underlying data that in turn helps in disease diagnosis and drug development. Particle Swarm Optimization (PSO) technique is extensively used for many practical applications but fails in finding the initial seeds to generate clusters and thus reduces the clustering accuracy. One of the meta-heuristic optimization algorithms called Harmony Search is free from divergence and helps to find out the near-global optimal solutions by searching the entire solution space. This paper proposes a novel Harmony Search Particle Swarm Optimization (HSPSO) clustering algorithm and is applied for Brain Tumor, Colon Cancer, Leukemia Cancer and Lung Cancer gene expression datasets for clustering. Experimental results show that the proposed algorithm produces clusters with better compactness and accuracy, in comparison with K-means clustering, PSO clustering (swarm clustering) and Fuzzy PSO clustering.