Data analysis of multiobjective density based spatial clustering schemes in gene selection process for cancer diagnosis

In the field of pattern recognition, the study of the gene expression profiles of different tissue samples over different experimental conditions has become feasible with the arrival of microarray-based technology. In cancer research, classification of tissue samples is necessary for cancer diagnosis, which can be done with the help of microarray technology. In this paper, we have presented a Multi Objective Optimization (MOO)-based clustering technique utilizing Archived Multi Objective Simulated Annealing (AMOSA) as the underlying optimization strategy for classification of tissue samples from cancer datasets. The presented clustering technique is evaluated for three open source Breast cancer, diabetes and hypothyroid datasets. In terms of evaluating the quality and the goodness of produced clusters, two cluster quality measures viz, adjusted rand index and Classification of Accuracy (%CoA) are calculated. The compared results of the presented clustering algorithm with ten state-of-the-art existing clustering techniques are shown for three datasets. Also, we have conducted a statistical significance test called t-test to prove the superiority of our presented MOO-based clustering technique over other clustering techniques and Density Based Spatial Clustering And Noise application (DBSCAN) important gene markers they identify and demonstrate the visual of clustering and their solutions.