The Rough Set k-Means Clustering
Seiki Ubukata, Akira Notsu, Katsuhiro Honda · 2016
In the field of clustering, soft computing approaches which deal with vague cluster memberships are effective. Clustering based on rough set theory is considered to be a promising approach as a way to represent vague cluster memberships as well as fuzzy clustering. In this paper, we propose the Rough Set k-Means (RSKM) clustering which is based on rough sets. The RSKM clustering is a type of the k-means clustering based on rough approximations and it enables certain clustering detecting the boundary and positive regions of temporal clusters. We carried out some numerical experiments and confirmed the performance and characteristics of the proposed method.