Evolutionary algorithms and fuzzy sets for discovering temporal rules

MATTHEWS, Stephen G., GONGORA, Mario A. and HOPGOOD, Adrian (2013). Evolutionary algorithms and fuzzy sets for discovering temporal rules. International Journal of Applied Mathematics and Computer Science, 23 (4), 855-868.

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Abstract

A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns are augmented into a dataset to analyse the method’s ability in a controlled experiment. It is shown that the method is capable of discovering temporal patterns, and the effect of Boolean itemset support on the efficacy of discovering temporal fuzzy association rules is presented.

Item Type:	Article
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/10.2478/amcs-2013-0064
Page Range:	855-868
Depositing User:	Helen Garner
Date Deposited:	08 Apr 2014 10:39
Last Modified:	18 Mar 2021 23:45
URI:	https://shura.shu.ac.uk/id/eprint/7928

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