一种基于区间数多指标信息的FCM聚类算法

A FCM clustering algorithm for multiple attribute information with interval numbers

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.