Discovering knowledge from data clustering using automatically-defined interval type-2 fuzzy predicates

作者：

Highlights：

• It is proposed a new clustering method based on interval type-2 fuzzy predicates.

• Fuzzy predicates are automatically generated from data describing clusters.

• Interval type-2 membership functions model variability and vagueness in clusters.

• Linguistic descriptions and knowledge are extracted from predicates.

• The method can be applied to data analysis applications.

摘要

• It is proposed a new clustering method based on interval type-2 fuzzy predicates.• Fuzzy predicates are automatically generated from data describing clusters.• Interval type-2 membership functions model variability and vagueness in clusters.• Linguistic descriptions and knowledge are extracted from predicates.• The method can be applied to data analysis applications.

论文关键词：Fuzzy predicates,Interval type-2 fuzzy logic,Clustering,Knowledge-discovering,Vagueness

论文评审过程：Received 28 August 2015, Revised 10 October 2016, Accepted 10 October 2016, Available online 12 October 2016, Version of Record 20 October 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.10.018