A novel modified particle swarm optimization algorithm with mutation for data clustering problem

Abstract

Particle Swarm Optimization (PSO) and K-Means (KM) are widely used for solving data clustering. KM encounters the problem of initializing the cluster centers and the problem of trapping in local optimum. When PSO is applied with KM, it can decrease two problems from KM. Hence, the hybrid clustering technique based on PSO and KM that can enhance performance of clustering is more than using KM alone. However, the hybrid clustering technique encounters the trapping in local optimum problem. To solve this problem, this paper proposed improving hybrid technique by the mutation operation is applied with particles of PSO when swarm traps in local optimum. The proposed technique is tested on eight datasets from the UCI Machine Learning Repository and gives more satisfied search results in comparison with PSOs for the data clustering problems.