Artículo
Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution
Autor/es | Martínez Ballesteros, María del Mar
Troncoso Lora, Alicia Martínez Álvarez, Francisco Riquelme Santos, José Cristóbal |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2010 |
Fecha de depósito | 2022-04-27 |
Publicado en |
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Resumen | 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 ... 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 projected to determine the intervals that define the rules without needing to discretize the attributes. The proposed algorithm is evaluated in synthetic datasets under different levels of noise in order to test its performance and the reported results are then compared to that of a multi-objective differential evolution algorithm, recently published. Furthermore, rules from real-world time series such as temperature, humidity, wind speed and direction of the wind, ozone, nitrogen monoxide and sulfur dioxide have been discovered with the objective of finding all existing relations between atmospheric pollution and climatological conditions. |
Agencias financiadoras | Ministerio de Ciencia Y Tecnología (MCYT). España Junta de Andalucía |
Identificador del proyecto | TIN2007-68084-C-00
P07-TIC-02611 |
Cita | Martínez Ballesteros, M.d.M., Troncoso Lora, A., Martínez Álvarez, F. y Riquelme Santos, J.C. (2010). Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution. Integrated Computer-Aided Engineering, 17 (3), 227-242. |
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