Evolutionary algorithms and fuzzy sets for discovering temporal rules

Stephen G. Matthews; Mario A. Gongora; Adrian A. Hopgood

International Journal of Applied Mathematics and Computer Science (2013)

  • Volume: 23, Issue: 4, page 855-868
  • ISSN: 1641-876X

Abstract

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A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns are augmented into a dataset to analyse the method's ability in a controlled experiment. It is shown that the method is capable of discovering temporal patterns, and the effect of Boolean itemset support on the efficacy of discovering temporal fuzzy association rules is presented.

How to cite

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Stephen G. Matthews, Mario A. Gongora, and Adrian A. Hopgood. "Evolutionary algorithms and fuzzy sets for discovering temporal rules." International Journal of Applied Mathematics and Computer Science 23.4 (2013): 855-868. <http://eudml.org/doc/262453>.

@article{StephenG2013,
abstract = {A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns are augmented into a dataset to analyse the method's ability in a controlled experiment. It is shown that the method is capable of discovering temporal patterns, and the effect of Boolean itemset support on the efficacy of discovering temporal fuzzy association rules is presented.},
author = {Stephen G. Matthews, Mario A. Gongora, Adrian A. Hopgood},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {fuzzy association rules; temporal association rules; multi-objective evolutionary algorithm},
language = {eng},
number = {4},
pages = {855-868},
title = {Evolutionary algorithms and fuzzy sets for discovering temporal rules},
url = {http://eudml.org/doc/262453},
volume = {23},
year = {2013},
}

TY - JOUR
AU - Stephen G. Matthews
AU - Mario A. Gongora
AU - Adrian A. Hopgood
TI - Evolutionary algorithms and fuzzy sets for discovering temporal rules
JO - International Journal of Applied Mathematics and Computer Science
PY - 2013
VL - 23
IS - 4
SP - 855
EP - 868
AB - A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns are augmented into a dataset to analyse the method's ability in a controlled experiment. It is shown that the method is capable of discovering temporal patterns, and the effect of Boolean itemset support on the efficacy of discovering temporal fuzzy association rules is presented.
LA - eng
KW - fuzzy association rules; temporal association rules; multi-objective evolutionary algorithm
UR - http://eudml.org/doc/262453
ER -

References

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