Evolving temporal fuzzy association rules from quantitative data with a multi-objective evolutionary algorithm

MATTHEWS, Stephen G., GONGORA, Mario A. and HOPGOOD, Adrian A. (2011). Evolving temporal fuzzy association rules from quantitative data with a multi-objective evolutionary algorithm. In: Hybrid Artificial Intelligent Systems. London, Springer, 198-205.

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Abstract

A novel method for mining association rules that are both quantitative and temporal using a multi-objective evolutionary algorithm is presented. This method successfully identifies numerous temporal association rules that occur more frequently in areas of a dataset with specific quantitative values represented with fuzzy sets. The novelty of this research lies in exploring the composition of quantitative and temporal fuzzy association rules and the approach of using a hybridisation of a multi-objective evolutionary algorithm with fuzzy sets. Results show the ability of a multi-objective evolutionary algorithm (NSGA-II) to evolve multiple target itemsets that have been augmented into synthetic datasets.

Item Type:	Book Section
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/10.1007/978-3-642-21219-2_26
Page Range:	198-205
Depositing User:	Adrian Hopgood
Date Deposited:	30 Aug 2012 15:55
Last Modified:	18 Mar 2021 04:46
URI:	https://shura.shu.ac.uk/id/eprint/5640

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