Temporal fuzzy association rule mining with 2-tuple linguistic representation

MATTHEWS, Stephen G., GONGORA, Mario A., HOPGOOD, Adrian A. and AHMADI, Samad (2012). Temporal fuzzy association rule mining with 2-tuple linguistic representation. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2012, Brisbane, Australia, 10-15 June 2012. 1-8.

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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.

Item Type:	Conference or Workshop Item (Paper)
Research Institute, Centre or Group - Does NOT include content added after October 2018:	Materials and Engineering Research Institute > Modelling Research Centre > Microsystems and Machine Vision Laboratory
Identification Number:	https://doi.org/10.1109/FUZZ-IEEE.2012.6251173
Page Range:	1-8
Depositing User:	Adrian Hopgood
Date Deposited:	24 Sep 2012 13:33
Last Modified:	18 Mar 2021 14:16
URI:	https://shura.shu.ac.uk/id/eprint/6227

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