A charged system search approach for data clustering
This paper presents a charged system search optimization method for finding the optimal cluster centers in a given dataset. CSS algorithm utilizes the Coulomb and Gauss laws from electrostatics to initiate the local search, and Newton second law of motion from mechanics is employed for global search. The efficiency and capability of the proposed algorithm are evaluated on seven datasets and compared with existing \(K\)-means, GA, PSO and ACO algorithms. From the experimental results, it is found that the proposed algorithm provides more accurate and effective results than other methods being compared.
clustering gauss law coulomb law charge particles newton law