DocumentCode :
3662555
Title :
Fuzzy data mining and expert system development
Author :
L.B. Turksen
Author_Institution :
Dept. of Mech. & Ind. Eng., Toronto Univ., Ont., Canada
Volume :
2
fYear :
1998
Firstpage :
2057
Abstract :
The proposed fuzzy data mining and expert system approach has two main modules: knowledge representation and approximate reasoning. The knowledge representation module is based on a modified fuzzy c-means (FCM) algorithm which is an extension of classical FCM algorithm in several respects. Hence, knowledge representation is developed with an unsupervised learning with respect to a given input-output data set. The approximate reasoning module contains four reasoning parameters that are subject to supervised learning for a given input-output data set and error minimization criteria.
Keywords :
"Hybrid intelligent systems","Fuzzy systems","Data mining","Expert systems","Input variables","Knowledge representation","Supervised learning","Unsupervised learning","Training data","Industrial engineering"
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on
ISSN :
1062-922X
Print_ISBN :
0-7803-4778-1
Type :
conf
DOI :
10.1109/ICSMC.1998.728201
Filename :
728201
Link To Document :
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