Show simple item record

dc.contributor.authorMatthews, Stephen G.en
dc.contributor.authorGongora, Mario Augustoen
dc.contributor.authorHopgood, Adrian A.en
dc.contributor.authorAhmadi, Samaden
dc.date.accessioned2012-08-23T13:03:49Z
dc.date.available2012-08-23T13:03:49Z
dc.date.issued2012
dc.identifier.citationMatthews, 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.isbn9781467315050
dc.identifier.issn1098-7584
dc.identifier.urihttp://hdl.handle.net/2086/6920
dc.description.abstractThis 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.sponsorshipEPSRC DTAen
dc.language.isoenen
dc.publisherIEEEen
dc.subjectgenetic algorithmsen
dc.subjectfuzzy logicen
dc.subjecthybriden
dc.subjectcomputational intelligenceen
dc.subjectsoft computingen
dc.subject2-tupleen
dc.subjectfuzzy association rule miningen
dc.subjecttemporal association rule miningen
dc.subjectdata miningen
dc.subjectknowledge discoveryen
dc.titleTemporal fuzzy association rule mining with 2-tuple linguistic representationen
dc.typeConferenceen
dc.identifier.doihttp://dx.doi.org/10.1109/FUZZ-IEEE.2012.6251173
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record