摘要
针对调和K均值聚类(KHM)算法存在陷入局部最优解的问题,提出一种人工蜂群(ABC)算法与KHM算法相结合的混合聚类算法ABCKHM.实验表明,该算法解决了KHM算法有时陷入局部最优解的问题,并且该算法较之KHM算法及同类其他算法有更好的性能.
It is still possible that K-harmonic means clustering algorithm reach local minimal value. Artificial bee colony optimization algorithm has good performance in global optimization, so the clustering algorithm derived from artificial bee colony optimization algorithm ought to overcome the problem of trapping in local optimization. This article proposes a hybrid clustering algorithm (ABCKHM) combining artificial bee colony algorithm and K-harmonic means algorithm. Our
出处
《东北师大学报(自然科学版)》
CAS
CSCD
北大核心
2017年第4期66-70,共5页
Journal of Northeast Normal University(Natural Science Edition)
基金
国家自然科学基金资助项目(71473035)
关键词
聚类
KHM
人工蜂群优化
ABC
clustering
K-harmonic means clustering
artificial bee colony optimization
artificial beecolony clustering