Auto-encoder based clustering algorithms for intuitionistic fuzzy sets - 文章详情页

作者：

Du, Yimin (Du, Yimin.) | Wu, Guixing (Wu, Guixing.) | Tang, Guolin (Tang, Guolin.)

收录：

EI Scopus

摘要：

Clustering　plays　an　important　role　in　data　mining　and　machine　learning.　Then,　intuitionistic　fuzzy　sets　(IFSs)　are　flexible　and　practical　in　dealing　with　vagueness　and　uncertainty　problems.　To　cluster　the　information　expressed　by　intuitionistic　fuzzy　data,　this　paper　proposes　the　joint　training　auto-encoder　based　intuitionistic　fuzzy　clustering　algorithm.　Firstly,　we　propose　the　auto-encoder　based　intuitionistic　fuzzy　clustering　by　utilizing　similarity　measure　of　IFSs,　auto-encoder　and　k-means　algorithm.　Then,　we　propose　the　joint　training　auto-encoder　based　intuitionistic　fuzzy　clustering　algorithm　by　utilizing　the　proposed　auto-encoder　based　intuitionistic　fuzzy　clustering　and　two　kinds　of　similarity　measures　for　the　clustering　analysis　of　intuitionistic　fuzzy　data.　Lastly,　several　experiments　are　provided　to　verify　the　effectiveness　of　the　proposed　intuitionistic　fuzzy　clustering　algorithms.　©　2017　IEEE.

关键词：

Fuzzy sets Signal encoding Intelligent systems K-means clustering Fuzzy clustering Data mining Cluster analysis Learning systems

作者机构：

[ 1 ] [Du, Yimin]School of Software Engineering, University of Science and Technology of China, Hefei, China
[ 2 ] [Wu, Guixing]School of Software Engineering, University of Science and Technology of China, Hefei, China
[ 3 ] [Tang, Guolin]School of Economics and Management, Beijing University of Technology, Beijing, China

通讯作者信息：

电子邮件地址：

查看成果更多字段

成果类型
所属机构

所有年份指定年份从至