Evolving Temporal Fuzzy Association Rules from Quantitative Data with a Multi-Objective Evolutionary Algorithm

Stephen G. Matthews*, Mario A. Gongora, Adrian A. Hopgood

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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.

Original languageEnglish
Title of host publicationHYBRID ARTIFICIAL INTELLIGENT SYSTEMS, PART I
EditorsE Corchado, M Kurzynski, M Wozniak
Place of PublicationBERLIN
PublisherSpringer-Verlag Berlin
Pages198-205
Number of pages8
ISBN (Print)978-3-642-21218-5
DOIs
Publication statusPublished - 2011
Event6th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2011) - Wroclaw, Poland
Duration: 23 May 201125 May 2011

Publication series

NameLecture Notes in Artificial Intelligence
PublisherSPRINGER-VERLAG BERLIN
Volume6678
ISSN (Print)0302-9743

Conference

Conference6th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2011)
CountryPoland
CityWroclaw
Period23/05/1125/05/11

Keywords

  • GENETIC ALGORITHM
  • NSGA-II
  • hyrbid
  • multi-objective evolutionary algorithm
  • fuzzy association rules
  • temporal association rules
  • DISCOVERY

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