Rough clustering with partial supervision

Abstract

This study focuses on bringing two rough-set-based clustering algorithms into the framework of partially supervised clustering. A mechanism of partial supervision relying on either fuzzy membership grades or rough memberships and non-memberships of patterns to clusters is envisioned. Allowing such knowledge-based hints to play an active role in the discovery of the overall structure of the dataset has proved to be highly beneficial, this being corroborated by the empirical results. Other existing rough clustering techniques can successfully incorporate this type of auxiliary information with little computational effort.