APPLYING HONEY-BEE MATING OPTIMIZATION AND PARTICLE SWARM OPTIMIZATION FOR CLUSTERING PROBLEMS

The use of information technologies in the various business areas is emerging in recent years. Mining the useful information existed in vast data has become an important issue. Clustering analysis which tries to segment data into homogeneous clusters is one of the most useful technologies in data mining methods. In this study, we proposed a clustering method which integrates particle swarm optimization with honey-bee mating optimization. Simulations for three benchmark test functions (MSE, intra-cluster distance and inter-cluster distance) are performed. According to the lowest MSE and the intra-cluster distance/inter-cluster distance value, experiment results show that our proposed method possesses better ability to find the global optimum than other well-known clustering algorithms.