摘要
在模糊C均值(Fuzzy C-Means,FCM)聚类应用过程中,针对目前模糊加权指数的确定缺乏理论依据和有效评价方法这一问题,提出了一种基于子集测度的模糊加权指数计算方法。首先根据子集测度理论定义了一个聚类有效性函数,然后依据该函数在聚类过程中通过循环进化迭代来计算聚类结果的有效性,并将其值反馈到模糊加权指数m的变化中,而使m收敛到一个稳定解,即得到最佳模糊加权指数。理论分析和实验表明,该算法是有效的,为模糊加权指数m的探讨研究提供了一种新的思路和途径。
As there still isn＇t any theoretical foundation or effective evaluation for the definition of fuzzy weighting exponent at present, a kind of algorithm of fuzzy weighting exponent based on subset measuring is presented during the application of Fuzzy C-Means （FCM）. Firstly, a clustering validity function is defined based on subset measuring theory, then the effectiveness of clustering results is iterated through evolution computing during the clustering process and give the feedback to the variation of fuzzy weighting exponent rn so that rn converges to a stable solution. Both theoretical analysis and experiments have shown that this algorithm is effective, and it provides a new ideas and approaches to the research of fuzzy weighted exponent.
出处
《模糊系统与数学》
CSCD
北大核心
2013年第2期136-141,共6页
Fuzzy Systems and Mathematics
基金
湖南省科技计划项目(2012FJ3036)
湖南省教育厅科研重点项目(12A042)
关键词
模糊加权指数
子集测度
聚类有效性
进化计算
Fuzzy Weighting Exponent
Subset Measuring
Clustering Validity
Evolution Computing
