A Modified Class Topper Algorithm and its Hybridization for Clustering Agriculture, Health-care and Geology Data

In this study, a modified class topper optimization algorithm named as MCTO is proposed for clustering problem. In the MCTO, cloning, crossover and mutation operators are introduced for global and faster convergence and better quality solutions. Two different types of hybridizations with K-means named as K-MCTO and MCTO-K are then performed with the MCTO to check the effectiveness. First, local optimal solutions obtained from the K-means is fed to the MCTO to get a global optimal solution. Then, another hybridization is done by feeding the solutions obtained from the MCTO to the K-means. Experimental study shows that the MCTO, K-MCTO and MCTO-K outperform various well-known state-of-the-arts. The algorithms are validated by clustering five standard data sets taken from agriculture, health-care and geology domains.