Integrating rough set principles in the graded possibilistic clustering

Abstract Applied to fuzzy clustering, the graded possibilistic model allows the soft transition from probabilistic to possibilistic memberships, constraining the memberships in a region that is narrower the closer to probabilistic the memberships are. The integration of rough sets principles in the graded possibilistic clustering aims to improve the flexibility and the performance of the graded possibilistic model, providing a further option for uncertainty modeling. Through the novel concept of the Rough Feasible Region, the proposed approach differentiates the projection of memberships in the core and in the boundary of each cluster, exploiting the indiscernibility relation typical of rough sets and allowing a more robust and efficient estimation of centroids. Tests on real data confirm its viability.

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