data mining;fuzzy set theory;genetic algorithms;search problems;NSGA-II;association rule mining;dataset;multiobjective evolutionary optimisation;multiobjective evolutionary search;quantitative data;quantitative fuzzy itemsets;synthetic datasets;temporal fuzzy itemsets;Itemsets;Lead;Evolutionary computing;fuzzy association rule mining;itemset mining;multiobjective;temporal association rule mining;
Abstract :
[en] We present a novel method for mining itemsets that are both quantitative and temporal, for association rule mining, using multi-objective evolutionary search and optimisation. This method successfully identifies temporal itemsets that occur more frequently in areas of a dataset with specific quantitative values represented with fuzzy sets. Current approaches preprocess data which can often lead to a loss of information. The novelty of this research lies in exploring the composition of quantitative and temporal fuzzy itemsets and the approach of using a multi-objective evolutionary algorithm. This preliminary work presents the problem, a novel approach and promising results that will lead to future work. Results show the ability of NSGA-II to evolve target itemsets that have been augmented into synthetic datasets. Itemsets with different levels of support have been augmented to demonstrate this approach with varying difficulties.,
Disciplines :
Computer science
Author, co-author :
Matthews, S. G.
Gongora, M. A.
Hopgood, Adrian ; Université de Liège > HEC - Ecole de gestion de l'ULG : Direction générale
Language :
English
Title :
Evolving temporal fuzzy itemsets from quantitative data with a multi-objective evolutionary algorithm,
Publication date :
2011
Event name :
5th Int. Workshop on Genetic & Evolutionary Fuzzy Systems
Event organizer :
IEEE
Event place :
Paris, France
Event date :
11-15 April 2011
Audience :
International
Main work title :
Genetic and Evolutionary Fuzzy Systems (GEFS), 2011 IEEE 5th International Workshop on,
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