A novel cluster validity criterion for fuzzy C-regression models
Chung‐Chun Kung, Jui-Yiao Su, Yi-Fen Nieh · 2009
This paper proposed a novel cluster validity criterion for fuzzy c-regression models (FCRM) clustering algorithm with hyper-plane-shaped clusters. We combined the concept of fuzzy hypervolume with the compactness validity function in the cluster validity criterion. The proposed cluster validity criterion determined the appropriate number of clusters by calculating the overall compactness and separateness of the FCRM partition. The simulation results demonstrated the validness and effectiveness of the proposed method.