Title :
A data mining approach for fuzzy classification rule generation
Author :
Wang, Dianhui ; Dillon, Tharam S. ; Chang, Elizabeth J.
Author_Institution :
Dept. of Comput., Hong Kong Polytech. Univ., Kowloon, China
Abstract :
This paper aims at developing a data mining approach for fuzzy classification rule generation. A regularization theory based theoretical framework for refining fuzzy classification rules is proposed. Our fuzzy rule induction methodology has four phases, namely: (1) ellipsoidal crisp rule generation with membership function assignment, (2) generic hyperbox fuzzy rule (GHR) derivation, (3) refinement of the GHR using a regularization model, and (4) simplification of the GHR by selecting an informative subset of premises out of the initial set
Keywords :
data mining; fuzzy logic; fuzzy set theory; pattern classification; data mining; ellipsoidal crisp rule generation; fuzzy classification; fuzzy logic; fuzzy membership functions; fuzzy model; fuzzy rule induction; fuzzy rules; fuzzy sets; generic hyperbox fuzzy rule derivation; linguistic interpretability; membership function assignment; refining fuzzy classification; regularization theory; rule generation; Data mining; Decision making; Fuzzy logic; Fuzzy sets; Induction generators; Input variables; Nonlinear control systems; Pattern recognition; Software engineering; Strontium;
Conference_Titel :
IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-7078-3
DOI :
10.1109/NAFIPS.2001.943698
