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

Authors:
Stephen G. Matthews;Mario A. Gongora;Adrian A. Hopgood
Affiliations:
Centre for Computational Intelligence, De Montfort University, Leicester, UK;Centre for Computational Intelligence, De Montfort University, Leicester, UK;Centre for Computational Intelligence, De Montfort University, Leicester, UK
Venue:
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Year:
2011

Citing 14
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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.