Temporal fuzzy association rule mining with 2-tuple linguistic representation
This paper reports on an approach that contributes towards the problem of discovering fuzzy association rules that exhibit a temporal pattern. The novel application of the 2-tuple linguistic representation identiﬁes fuzzy association rules in a temporal context, whilst maintaining the interpretability of linguistic terms. Iterative Rule Learning (IRL) with a Genetic Algorithm (GA) simultaneously induces rules and tunes the membership functions. The discovered rules were compared with those from a traditional method of discovering fuzzy association rules and results demonstrate how the traditional method can loose information because rules occur at the intersection of membership function boundaries. New information can be mined from the proposed approach by improving upon rules discovered with the traditional method and by discovering new rules.
Citation : Matthews, S. G. and Gongora, M. A., Hopgood, A. A. and Ahmadi, S. (2012) Temporal Fuzzy Association Rule Mining with 2-tuple Linguistic Representation. In: Proceedings of The 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2012), Brisbane, June 2012, pp. 1-8.
ISBN : 9781467315050
ISSN : 1098-7584
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes