Hybrid particle swarm optimisation for data clustering

Finding a best clustering algorithm to tackle the problem of finding the optimal partition of a data set is always an NP-hard problem. In general, solutions to the NP-hard problems involve searches through vast spaces of possible solutions and evolutionary algorithms have been a success. In this paper, we explore one such approach which is hardly known outside the search heuristic field - the Particle Swarm Optimisation+k-means (PSOk) for this purpose. The proposed hybrid algorithm consists of two modules, the PSO module and the k-means module. For the initial stage, the PSO module is executed for a short period to search for the clusters centroid locations. Succeeding to the PSO module is the refining stage where the detected locations are transferred to the k-means module for refinement and generation of the final optimal clustering solution. Experimental results on two challenging datasets and a comparison with other hybrid PSO methods has demonstrated and validated the effectiveness of the proposed solution in terms of precision and computational complexity.