Effects of Semi-supervised Learning on Rough Set-Based C-Means Clustering
Seiki Ubukata, Takeaki Shimizu, Akira Notsu, Katsuhiro Honda · 2018
As soft computing extensions of hard C-means (HCM) clustering, rough C-means (RCM) and rough set C-means (RSCM), which can deal with positive and possible cluster memberships based on rough set theory, have been proposed and utilized for detecting vague boundaries among clusters. Semi-supervised clustering schemes that utilize information of partial labeled objects are promising approaches for improving the classification performance. In this study, we consider how to introduce semi-supervised clustering schemes to RCM and RSCM. Furthermore, we confirm the effectiveness of the proposed methods through numerical experiments.