Rough possibilistic C-means clustering based on multigranulation approximation regions and shadowed sets
作者：
Highlights：
• A novel rough possibilistic C-means clustering approach is presented.
• The partition threshold for each cluster is automatically optimized.
• The uncertainty generated by a single fuzzifier value is captured.
• The scale parameters are adaptively adjusted.
• A framework for updating prototypes based on ensemble strategies is formed.
摘要
•A novel rough possibilistic C-means clustering approach is presented.•The partition threshold for each cluster is automatically optimized.•The uncertainty generated by a single fuzzifier value is captured.•The scale parameters are adaptively adjusted.•A framework for updating prototypes based on ensemble strategies is formed.
论文关键词：Shadowed sets,Rough sets,Granular computing,Possibilistic C-means,Multigranulation approximation regions
论文评审过程：Received 14 February 2018, Revised 3 July 2018, Accepted 4 July 2018, Available online 5 July 2018, Version of Record 12 September 2018.
论文官网地址：https://doi.org/10.1016/j.knosys.2018.07.007
