ECMdd: Evidential c-medoids clustering with multiple prototypes
作者：
Highlights：
• Extend medoid-based clustering algorithm on the framework of belief functions.
• Introduce imprecise clusters which enable us to make soft decisions for uncertain data.
• Use multiple weighted prototypes to capture various types of class structure.
• Experimental results confirm the superiority of the proposed clustering algorithms.
摘要
Highlights•Extend medoid-based clustering algorithm on the framework of belief functions.•Introduce imprecise clusters which enable us to make soft decisions for uncertain data.•Use multiple weighted prototypes to capture various types of class structure.•Experimental results confirm the superiority of the proposed clustering algorithms.
论文关键词：Credal partitions,Relational clustering,Multiple prototypes,Imprecise classes
论文评审过程：Received 25 March 2015, Revised 6 May 2016, Accepted 15 May 2016, Available online 24 May 2016, Version of Record 6 June 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.05.005
