@inproceedings{b3047ea17e304cdca28c89361b12f699,
title = "Evolving Temporal Fuzzy Association Rules from Quantitative Data with a Multi-Objective Evolutionary Algorithm",
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.",
keywords = "GENETIC ALGORITHM, NSGA-II, hyrbid, multi-objective evolutionary algorithm, fuzzy association rules, temporal association rules, DISCOVERY",
author = "Matthews, \{Stephen G.\} and Gongora, \{Mario A.\} and Hopgood, \{Adrian A.\}",
year = "2011",
doi = "10.1007/978-3-642-21219-2\_26",
language = "English",
isbn = "978-3-642-21218-5",
series = "Lecture Notes in Artificial Intelligence",
publisher = "Springer-Verlag Berlin",
pages = "198--205",
editor = "E Corchado and M Kurzynski and M Wozniak",
booktitle = "HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, PART I",
note = "6th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2011) ; Conference date: 23-05-2011 Through 25-05-2011",
}