Temporal fuzzy association rule mining with 2-tuple linguistic representation

De Montfort University Open Research Archive

Show simple item record

dc.contributor.author Matthews, Stephen G. en
dc.contributor.author Gongora, Mario Augusto en
dc.contributor.author Hopgood, Adrian A. en
dc.contributor.author Ahmadi, Samad en
dc.date.accessioned 2012-08-23T13:03:49Z
dc.date.available 2012-08-23T13:03:49Z
dc.date.issued 2012
dc.identifier.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. en
dc.identifier.isbn 9781467315050
dc.identifier.issn 1098-7584
dc.identifier.uri http://hdl.handle.net/2086/6920
dc.description.abstract 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. en
dc.description.sponsorship EPSRC DTA en
dc.language.iso en en
dc.publisher IEEE en
dc.subject genetic algorithms en
dc.subject fuzzy logic en
dc.subject hybrid en
dc.subject computational intelligence en
dc.subject soft computing en
dc.subject 2-tuple en
dc.subject fuzzy association rule mining en
dc.subject temporal association rule mining en
dc.subject data mining en
dc.subject knowledge discovery en
dc.title Temporal fuzzy association rule mining with 2-tuple linguistic representation en
dc.type Conference en
dc.identifier.doi http://dx.doi.org/10.1109/FUZZ-IEEE.2012.6251173
dc.researchgroup Centre for Computational Intelligence en
dc.peerreviewed Yes en

Files in this item

This item appears in the following Collection(s)

Show simple item record