Uncertainty mode selection in categorical clustering using the rough set theory

作者：

Highlights：

• Using Rough Set Theory to find most suitable modes in each iteration of the clustering process.

• The notion of density is also used to identify the most accurate mode.

• The proposed method is compared to the k-modes and more recent methods and showed great efficiency.

摘要

•Using Rough Set Theory to find most suitable modes in each iteration of the clustering process.•The notion of density is also used to identify the most accurate mode.•The proposed method is compared to the k-modes and more recent methods and showed great efficiency.

论文关键词：Unsupervised learning,Categorical clustering,Rough set theory,K-modes,Uncertainty

论文评审过程：Received 18 June 2019, Revised 8 April 2020, Accepted 9 May 2020, Available online 30 May 2020, Version of Record 24 June 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113555