Title :
Fuzzy System Based on Class Association Rules
Author :
Jia, Ren ; Yibo, Zhang
Author_Institution :
Zhejiang Sci-Tech Univ., Hangzhou
Abstract :
Fuzzy system has been proved to be a universal approximator, yet the curse of dimensionality is still the unsolved problem for it. Class association rules (CARs) are interesting and frequent patterns derived from data through adapted Apriori algorithm. Using CARs to build the fuzzy rule base of fuzzy system can solve the curse of dimensionality problem effectively. Thus, a novel fuzzy system based on CARs is proposed in this paper. The process of how to build the fuzzy system and its whole execution are presented in detail. Furthermore, comparative experiments are also made to prove the effectiveness of the presented strategy.
Keywords :
data mining; fuzzy systems; knowledge based systems; class association rules; dimensionality problem; fuzzy rule base; fuzzy system; universal approximator; Association rules; Automation; Buildings; Data mining; Data preprocessing; Fuzzy systems; Input variables; Predictive models; Shape; Training data;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2874-8
DOI :
10.1109/FSKD.2007.338
