K-medoids clustering algorithm method based on differential evolution
一种基于差分演化的K-medoids聚类算法

The traditional K-medoids clustering algorithm, because on the initial clustering center sensitive, the global search ability is poor, easily trapped into local optimal, slow convergent speed, and so on. Therefore, this paper proposed a kind of K-medoids clustering algorithm based on differential evolution. Differential evolution was a kind of heuristic global search technology population, had strong robustness. It combined with the global optimization ability of differential evolution using K-medoids clustering algorithm, effectively overcame K-medoids clustering algorithm, shortend convergence time, improved clustering quality. Finally, the simulation result shows that the algorithm is verified stability and robustness.