Effects of Semi-supervised Learning on Rough Set C-Means Clustering

Abstract

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 not only unlabeled objects but also partial labeled objects are promising approaches for improving the classification performance. In this study, we consider how to introduce semi-supervised approaches to RSCM clustering. Furthermore, we confirm the effectiveness of the proposed method through numerical experiments.