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Selecting the best measures to discover quantitative association rules

Opened Access Selecting the best measures to discover quantitative association rules

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Autor: Martínez Ballesteros, María del Mar
Martínez Álvarez, Francisco
Troncoso Lora, Alicia
Riquelme Santos, José Cristóbal
Departamento: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Fecha: 2014
Publicado en: Neurocomputing, 126, 3-14.
Tipo de documento: Artículo
Resumen: The majority of the existing techniques to mine association rules typically use the support and the confidence to evaluate the quality of the rules obtained. However, these two measures may not be sufficient to properly assess their quality due to some inherent drawbacks they present. A review of the literature reveals that there exist many measures to evaluate the quality of the rules, but that the simultaneous optimization of all measures is complex and might lead to poor results. In this work, a principal components analysis is applied to a set of measures that evaluate quantitative association rules' quality. From this analysis, a reduced subset of measures has been selected to be included in the fitness function in order to obtain better values for the whole set of quality measures, and not only for those included in the fitness function. This is a general-purpose methodology and can, therefore, be applied to the fitness function of any algorithm. To validate if better re...
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Cita: Martínez Ballesteros, M.d.M., Martínez Álvarez, F., Troncoso Lora, A. y Riquelme Santos, J.C. (2014). Selecting the best measures to discover quantitative association rules. Neurocomputing, 126, 3-14.
Tamaño: 608.0Kb
Formato: PDF

URI: http://hdl.handle.net/11441/43558

DOI: http://dx.doi.org/10.1016/j.neucom.2013.01.056

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