摘要
针对标准K-medoids算法在大数据聚类应用中易陷入局部最优解以及聚类效果受初始中心限制的缺点,提出了基于遗传模拟退火算法的K-medoids改进算法。该算法结合遗传算法和模拟退火算法,可以增强标准K-medoids算法在聚类时的全局搜索能力,并加快其收敛速度。对比实验证明:这一改进有效地弥补了标准K-medoids算法的上述缺陷,达到了提高聚类效率、加快收敛速度、改善聚类质量的目的。
Standard K-medoids algorithm has the disadvantages of easy-to-fall into local optima and the clustering effect is commonly influenced by the initial cluster centre. To overcome these shortcomings,a modified K-medoids algorithm is proposed which is based on the genetic simulated annealing algorithm.By combing the genetic algorithm and the annealing algorithm,the global search ability and convergence speed of the proposed algorithm are greatly improved.Comparison experiment results show that the modified algorithm can effectively overcome the shortcomings of the Standard Kmedoids algorithm that the clustering efficiency,convergence speed and clustering quality are improved.
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2015年第2期619-623,共5页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金项目(60973041)
