DocumentCode
1630663
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
fYear
2009
Firstpage
1885
Lastpage
1890
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on
Conference_Location
Jeju Island
ISSN
1098-7584
Print_ISBN
978-1-4244-3596-8
Electronic_ISBN
1098-7584
Type
conf
DOI
10.1109/FUZZY.2009.5277386
Filename
5277386
Link To Document