Interpretable interval type-2 fuzzy predicates for data clustering: A new automatic generation method based on self-organizing maps

作者：

Highlights：

• A new clustering based on interval type-2 fuzzy predicates and SOMs is proposed.

• SOMs are automatically configured and trained.

• Fuzzy predicates are generated using cluster prototypes extracted from SOMs.

• Linguistic knowledge is obtained from the predicates automatically generated.

• The proposed method overcome existing clustering methods based on fuzzy predicates.

摘要

•A new clustering based on interval type-2 fuzzy predicates and SOMs is proposed.•SOMs are automatically configured and trained.•Fuzzy predicates are generated using cluster prototypes extracted from SOMs.•Linguistic knowledge is obtained from the predicates automatically generated.•The proposed method overcome existing clustering methods based on fuzzy predicates.

论文关键词：Fuzzy predicates,Interval type-2 fuzzy logic,Self-organizing maps,Interpretable clustering,Knowledge discovery

论文评审过程：Received 17 February 2017, Revised 6 June 2017, Accepted 13 July 2017, Available online 14 July 2017, Version of Record 4 September 2017.

论文官网地址：https://doi.org/10.1016/j.knosys.2017.07.012