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Mostrando ítems 1-6 de 6
Artículo
Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets
(iOS Press, 2015)
Association rule mining is a well-known methodology to discover significant and apparently hidden relations among attributes in a subspace of instances from datasets. Genetic algorithms have been extensively used to find ...
Artículo
Discovering gene association networks by multi-objective evolutionary quantitative association rules
(Elsevier, 2014)
In the last decade, the interest in microarray technology has exponentially increased due to its ability to monitor the expression of thousands of genes simultaneously. The reconstruction of gene association networks ...
Artículo
An evolutionary algorithm to discover quantitative association rules in multidimensional time series
(Springer, 2011)
An evolutionary approach for finding existing relationships among several variables of a multidimensional time series is presented in this work. The proposed model to discover these relationships is based on quantitative ...
Artículo
Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution
(IOS Press, 2010)
This research presents the mining of quantitative association rules based on evolutionary computation techniques. First, a real-coded genetic algorithm that extends the well-known binary-coded CHC algorithm has been ...
Artículo
Improving a multi-objective evolutionary algorithm to discover quantitative association rules
(Springer, 2015)
This work aims at correcting flaws existing in multi-objective evolutionary schemes to discover quantitative association rules, specifically those based on the wellknown non-dominated sorting genetic algorithm-II (NSGA-II). ...
Artículo
Discovering quantitative association rules: A novel approach based on evolutionary algorithms
(IOS Press, 2014)
This work proposes a novel methodology to improve the discovery of quantitative association rules in continuous datasets. This methodology comprises several evolutionary algorithms able to deal with real-valued variables ...