首页 | 本学科首页   官方微博 | 高级检索  
     检索      

一种基于改进微粒群和轮廓系数的划分聚类方法
引用本文:王学贺.一种基于改进微粒群和轮廓系数的划分聚类方法[J].云南民族大学学报(自然科学版),2016(4):367-371.
作者姓名:王学贺
作者单位:菏泽医学专科学校计算机教研室
摘    要:为解决聚类问题中簇的个数不易确定的难题,提出一种自动化的聚类方法.该方法针对不确定的簇个数,给出了一种新的粒子表示方法,并利用微粒群算法在完成一次聚类后,再利用kmeans算法重新分配数据对象并计算聚类中心.该方法利用结合凝聚度和分离度概念的轮廓系数来确定簇的个数,并用误差平方和来辅助验证.实验表明,该方法可以找到最佳的簇个数,并可以有效的对数据对象进行聚类.

关 键 词:聚类  凝聚度  分离度  误差平方总和  微粒群

An automatic approach to solving clustering problems with the number of clusters unknown based on the particle swarm optimization and silhouette coefficient
WANG Xue-he.An automatic approach to solving clustering problems with the number of clusters unknown based on the particle swarm optimization and silhouette coefficient[J].Journal of Yunnan Nationalities University:Natural Sciences Edition,2016(4):367-371.
Authors:WANG Xue-he
Institution:WANG Xue-he;Department of Computer Sciences,Heze Medical College;
Abstract:Clustering is an important technology that can divide data patterns into meaningful groups,but the number of groups is difficult to be determined. This paper gives an automatic approach,which can determine the number of groups by using the silhouette coefficient and the sum of the squared errors,and can cluster the data patterns through using the particle swarm optimization and k- means. This approach gives a new particle representation and uses the cohesion and separation of the clusters in the silhouette coefficient to determine the number of the clusters.The experiment conducted shows that the proposed approach can help find the optimum number of clusters,and can cluster the data patterns effectively.
Keywords:cohesion  separation  sum of the squared errors  particle swarm optimization
本文献已被 CNKI 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号