%0 Journal Article
%T K-medoids clustering algorithm method based on differential evolution<br>一种基于差分演化的K-medoids聚类算法
%A MENG Ying
%A LUO Ke
%A LIU Jian-hua
%A SHI Shuangc
%A <br>孟颖
%A 罗可
%A 刘建华
%A 石爽c
%J 计算机应用研究
%D 2012
%I 
%X 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.
%K differential evolution(DE)
%K cluster quality
%K K-medoids algorithm
%K global optimization<br>差分演化
%K 聚类质量
%K Kmedoids算法
%K 全局优化
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=F886A295C1E57CC5DF0A570B52F5E8AA&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=94C357A881DFC066&sid=1CF15D8E59774A81&eid=4C9B788AD3F48EA4&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=11