Abstract
Rough C-means (RCM), rough set C-means (RSCM), and rough membership C-means (RMCM) are extensions of hard C-means (HCM) clustering based on rough set theory, and can deal with positive, possible, and uncertain belonging of objects to clusters. RSCM and RMCM are clustering models on the approximation space granulated by a binary relation, and the granularity of the object space is considered. Rough membership is the conditional probability of a cluster in the neighborhood of an object, and is useful as a probabilistic cluster membership. For RCM and RSCM, the usefulness of semi-supervised learning, in which partial labeled objects are used as supervision, has been reported. In this study, we propose semi-supervised RMCM methods and verify their effects by numerical experiments.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
