A Clustering Approach Based on Charged Particles
In pattern recognition, clustering is a powerful technique that can be used to find theidentical group of objects from a given dataset. It has proven its importance in variousdomains such as bioinformatics, machine learning, pattern recognition, documentclustering and so on. But, in clustering, it is difficult to determine the optimal clustercenters in a given set of data. So, in this paper, a new method called magnetic chargedsystem search (MCSS) is applied to determine the optimal cluster centers. This methodbased on the behavior of charged particles. The proposed method employs the electricforce and magnetic force to initiate the local search while Newton second law of motionis employed for global search. The performance of the proposed algorithm is tested onseveral datasets which are taken from UCI repository and compared with the otherexisting methods like K-Means, GA, PSO, ACO and CSS. The experimental results provethe applicability of the proposed method in clustering domain.