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Evolving Temporal Fuzzy Association Rules from Quantitative Data with a Multi-Objective Evolutionary Algorithm
Matthews, Stephen G.; Gongora, Mario A.; Hopgood, Adrian
2011In Lecture Notes in Computer Science, 6678, p. 198-205
Peer reviewed
 

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Keywords :
multi-objective evolutionary algorithm; fuzzy association rules; temporal association rules; NSGA-II; hyrbid
Abstract :
[en] 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.
Disciplines :
Computer science
Author, co-author :
Matthews, Stephen G.;  De Montfort Univ, Ctr Computat Intelligence, Leicester LE1 9BH, Leics, England.
Gongora, Mario A.;  De Montfort Univ, Ctr Computat Intelligence, Leicester LE1 9BH, Leics, England.
Hopgood, Adrian ;  Université de Liège > HEC - Ecole de gestion de l'ULG : Direction générale
Language :
English
Title :
Evolving Temporal Fuzzy Association Rules from Quantitative Data with a Multi-Objective Evolutionary Algorithm
Publication date :
2011
Event name :
6th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2011)
Event organizer :
Wroclaw Univ Technol, IEEE Systems, Man & Cybernet Soc, Spanish Chapter, IEEE Systems, Man & Cybernet Soc, Czech Republ Chapter, Spanish Assoc Artificial Intelligence, MIR LABS, Int Federat Computat Logic
Event place :
Wroclaw Univ Technol, Wroclaw, Poland
Event date :
MAY 23-25, 2011
Audience :
International
Journal title :
Lecture Notes in Computer Science
ISSN :
0302-9743
eISSN :
1611-3349
Publisher :
Springer-Verlag Berlin, Berlin, Germany
Special issue title :
HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, PART I
Volume :
6678
Pages :
198-205
Peer reviewed :
Peer reviewed
Commentary :
978-3-642-21218-5 Wroclaw Univ Technol Corchado, E Kurzynski, M Wozniak, M
Available on ORBi :
since 11 February 2016

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