Hierarchical hesitant fuzzy K-means clustering algorithm

查看全文 作　　者：CHEN [1,2]Na;XU Ze-[1,3]shui;XIA Mei-[4]mei

机构地区：[1]School of Economics and Management, Southeast University;[2]School of Applied Mathematics, Nanjing University of Finance and Economics;[3]Business School, Sichuan University;[4]School of Economics and Management, Tsinghua University

出　　处：《Applied Mathematics(A Journal of Chinese Universities)》索引2014年第29卷第1期,共17页

基　　金：Supported by the National Natural Science Foundation of China(61273209) 摘　　要：Due to the limitation and hesitation in one's knowledge,the membership degree of an element to a given set usually has a few different values,in which the conventional fuzzy sets are invalid. Hesitant fuzzy sets are a powerful tool to treat this case. The present paper focuses on investigating the clustering technique for hesitant fuzzy sets based on the K-means clustering algorithm which takes the results of hierarchical clustering as the initial clusters. Finally,two examples demonstrate the validity of our algorithm. 关键词：K-均值聚类算法模糊集 K-MEANS聚类算法聚类技术层次聚类隶属度簇

相关文献

参考文献（40）
引证文献（21）
耦合文献（93）

参考文献(40)

引证文献(21)

耦合文献(93)

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费

Hierarchical hesitant fuzzy K-means clustering algorithm

参考文献(40)

引证文献(21)

耦合文献(93)

用户提示