DocumentCode
2668909
Title
Extracting fuzzy sparse rules by Cartesian representation and clustering
Author
Yam, Yeung ; Kreinovich, Vladik ; Nguyen, Hung T.
Author_Institution
Chinese Univ. of Hong Kong, Shatin, China
Volume
5
fYear
2000
fDate
2000
Firstpage
3778
Abstract
Sparse rule base and interpolation have been proposed as possible solution to alleviate the geometric complexity problem of large fuzzy set. However, no formal method to extract sparse rule base is yet available. This paper combines the recently introduced Cartesian representation of membership functions and a mountain method-based clustering technique for the extraction. A case study is included to demonstrate the effectiveness of the approach
Keywords
computational complexity; computational geometry; fuzzy logic; interpolation; Cartesian representation; clustering; fuzzy sparse rules extraction; geometric complexity problem; interpolation; large fuzzy set; membership functions; sparse rule base; Bismuth; Data mining; Fitting; Fuzzy sets; Interpolation; Lagrangian functions; Lapping; Least squares methods; Polynomials;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man, and Cybernetics, 2000 IEEE International Conference on
Conference_Location
Nashville, TN
ISSN
1062-922X
Print_ISBN
0-7803-6583-6
Type
conf
DOI
10.1109/ICSMC.2000.886598
Filename
886598
Link To Document