摘要
针对现有集成方法在处理模糊聚类时的不足,提出一种新的模糊聚类集成方法。在证据合成的理论框架内,讨论在识别框架、概率分配函数、合成规则等问题。给出了3种基本概率分配方法:近似类别分配概率方法、归一化模糊海明距离方法以及二者证据合成的方法。分析指出合成的方法能够较好利用二者的优势进行互补,获取更为合理的基本概率分配方法。最后,通过实验讨论所提方法的参数设置、收敛性和有效性等问题。
In order to overcome the weakness of present ensemble methods for fuzzy clustering,a novel method of fuzzy clustering ensemble is proposed.In the framework of evidence ensemble,the recognition framework,basic probability assignment and synthesis rules are analyzed.Three different basic probability assignment（BPA）methods is proposed,which are approximation probability of clusters,normalized fuzzy Hamming distance,and evidence synthesis of the two.Because of complementary advantages of the two methods,the third method is able to get more useful BPA.At last,parameter settings,convergence and effectiveness of the method proposed are analyzed by experiment.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2014年第7期1446-1452,共7页
Systems Engineering and Electronics
基金
国家自然科学基金(60975026
61273275)资助课题
关键词
模糊聚类集成
D-S证据理论
互相关矩阵
模糊海明距离
fuzzy clustering ensemble
Dempster-Shafer theory
co-association matrix
fuzzy hamming distance
