Title :
A novel cluster validity criterion for fuzzy C-regression models
Author :
Kung, Chung-Chun ; Su, Jui-Yiao ; Nieh, Yi-Fen
Author_Institution :
Dept. of Electr. Eng., Tatung Univ., Taipei, Taiwan
Abstract :
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.
Keywords :
fuzzy set theory; pattern clustering; regression analysis; cluster validity criterion; compactness validity function; fuzzy c-regression model clustering algorithm; fuzzy hypervolume; hyper-plane-shaped clusters; Algorithm design and analysis; Clustering algorithms; Entropy; Partitioning algorithms; fuzzy c-regression model (FCRM); fuzzy hypervolume;
Conference_Titel :
Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on
Conference_Location :
Jeju Island
Print_ISBN :
978-1-4244-3596-8
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZY.2009.5277386
