摘要
针对一类具有不确定性区间数多指标信息的聚类分析问题,基于传统的数值信息FCM(fuzzyc_means)聚类算法,提出了一种新的聚类分析算法.首先描述了具有区间数多指标信息的聚类分析问题,其次提出并证明了基于区间数多指标信息的关于最优划分和最优聚类中心确定的两个定理.然后根据提出的两个定理,进一步给出了基于区间数信息的FCM聚类算法的迭代步骤.最后,通过一个算例说明了给出的聚类算法.
With respect to multiple attribute clustering analysis problems with uncertain interval numbers, based on the traditional FCM(fuzzy c_means) clustering algorithm, a new clustering analysis algorithm is proposed. In this paper, firstly, the multiple attribute clustering analysis problem with interval numbers is introduced. Secondly, two theorems for determining the optimal partition and the optimal clustering center are proposed and proved. Then, based on the proposed two theorems, calculation steps of the FCM clustering algorithm for multiple attribute information with interval numbers are presented. Finally, a numerical example is also used to illustrate the applicability of the FCM clustering algorithm proposed in this paper.
出处
《系统工程学报》
CSCD
2004年第4期387-393,共7页
Journal of Systems Engineering
基金
国家自然科学基金资助项目(70071004)
教育部高等学校优秀青年教师教学科研奖励计划资助项目(教人司[2002]123).
关键词
聚类分析
区间数
FCM聚类算法
模糊集
clustering analysis
interval number
FCM clustering algorithm
fuzzy set