BHOHS: A Two Stage Novel Algorithm for Data Clustering

Abstract

Data clustering is a collection of data objects that are similar to one another within in the same cluster and are dissimilar to the objects in other clusters. In this paper we proposed new algorithm called black hole optimization with a heuristic search algorithm to overcome the problem of the BHO algorithm. The black hole optimization is used to produce an initial solution to the clustering problem and then a heuristic search algorithm is applied to improve the quality of this solution by searching around it. The black hole optimization algorithm starts with an initial population of candidate solutions to an optimization problem and for each star, evaluate the objective function. Then at each iteration of the black hole optimization algorithm, the best star is selected to be the black hole and all other candidate forms the normal stars. If a star moves too close to the black hole, it will be absorbed by the black hole and permanently disappear. The black hole optimization algorithm converges rapidly during the starting stage of the search process, but it goes near global optimal solution, the convergence speed also will decrease. So, it may get trapped in local optimal over a certain number of iterations. To evaluate the performance of BHOHS can be compared with k-means, particle swarm optimization (PSO) algorithm and black hole optimization algorithm (BHO).