基于改进引力搜索算法的K-means聚类  

Novel K-means clustering algorithm based on improved gravitational search algorithm

作　　者：魏康园 何庆 徐钦帅[1,2] Wei Kangyuan;He Qing;Xu Qinshuai(College of Big Data&Information Engineering,Guizhou University,Guiyang 550025,China;Guizhou Provincial Key Laboratory of Public Big Data,Guizhou University,Guiyang 550025,China)

机构地区：[1]贵州大学大数据与信息工程学院,贵阳550025 [2]贵州大学贵州省公共大数据重点实验室,贵阳550025

出　　处：《计算机应用研究》2019年第11期3240-3244,共5页Application Research of Computers

基　　金：贵州省公共大数据重点实验室开放课题(2017BDKFJJ004);贵州省教育厅青年科技人才成长项目(黔科合KY字[2016]124);贵州大学培育项目(黔科合平台人才[2017]5788)

摘　　要：针对K-means算法的聚类结果极易受到聚类中心的影响而陷入局部最优解的问题,提出一种基于改进引力搜索的K-means聚类算法。首先引入自适应概念,对引力系数衰减因子进行控制,提高算法的全局探索能力和局部开发能力;然后,引入免疫克隆选择机制,以便算法能够有效跳出局部最优,并通过对12个基准测试函数的实验验证改进引力搜索算法的有效性和优越性;最后,通过结合改进的引力搜索算法和K-means算法,提出一种新的聚类算法A2F-GSA-Kmeans,在六个测试数据集上的实验表明,该算法具有较好的聚类质量。In order to solve the problem that the clustering result of K-means algorithm gets affected by the initial cluster centers easily,this paper proposed a novel K-means clustering algorithm based on improved gravitational search algorithm.Firstly,it enhanced the global exploration and local exploitation capability of the algorithm with the introduction of adaptive concept to control the attenuation factor of gravitational constant.Then,it introduced immune clonal selection algorithm to make the algorithm jump out of the local optimum efficiently.The experimental results on twelve test functions prove the effectiveness and superiority of the improved GSA.Finally,by combining the improved GSA with K-means algorithm,this paper proposed a new clustering algorithm called A2F-GSA-Kmeans.The experimental results on six test datasets show that the algorithm has better clustering quality.

关 键 词：K-MEANS算法 引力搜索算法 引力系数衰减因子 免疫克隆选择算法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]