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Obtaining optimal quality measures for quantitative association rules

Acceso restringido Obtaining optimal quality measures for quantitative association rules

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Autor: 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: 2016
Publicado en: Neurocomputing, 176, 36-47.
Tipo de documento: Artículo
Resumen: There exist several works in the literature in which fitness functions based on a combination of weighted measures for the discovery of association rules have been proposed. Nevertheless, some differences in the measures used to assess the quality of association rules could be obtained according to the values of the weights of the measures included in the fitness function. Therefore, user's decision is very important in order to specify the weights of the measures involved in the optimization process. This paper presents a study of well-known quality measures with regard to the weights of the measures that appear in a fitness function. In particular, the fitness function of an existing evolutionary algorithm called QARGA has been considered with the purpose of suggesting the values that should be assigned to the weights, depending on the set of measures to be optimized. As initial step, several experiments have been carried out from 35 public datasets in order to show how the ...
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Cita: Martínez Ballesteros, M.d.M., Troncoso Lora, A., Martínez Álvarez, F. y Riquelme Santos, J.C. (2016). Obtaining optimal quality measures for quantitative association rules. Neurocomputing, 176, 36-47.
Tamaño: 756.4Kb
Formato: PDF
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URI: http://hdl.handle.net/11441/43608

DOI: http://dx.doi.org/10.1016/j.neucom.2014.10.100

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