SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts
ECML '93 Proceedings of the European Conference on Machine Learning
First approach toward on-line evolution of association rules with learning classifier systems
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Rough particle swarm optimization and its applications in data mining
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue (1143 - 1198) " Distributed Bioinspired Algorithms"; Guest editors: F. Fernández de Vega, E. Cantú-Paz
Expert Systems with Applications: An International Journal
Analysis of measures of quantitative association rules
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
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This work presents the discovering of association rules based on evolutionary techniques in order to obtain relationships among correlated time series. For this purpose, a genetic algorithm has been proposed to determine the intervals that form the rules without discretizing the attributes and allowing the overlapping of the regions covered by the rules. In addition, the algorithm has been tested on real-world climatological time series such as temperature, wind and ozone and results are reported and compared to that of the well-known Apriori algorithm.