Comparación de la Discretización Estándar con un Nuevo Método para Reglas de Asociación Cuantitativas (Comparison of Standard Discretization with a New Method for Quantitative Association Rules)

Juan Luis Domínguez Olmedo (juan.dominguez@dti.uhu.es)1, Jacinto Mata Vázquez (jacinto.mata@dti.uhu.es)1


1Universidad de Huelva

This paper appears in: Revista IEEE América Latina

Publication Date: April 2016
Volume: 14,   Issue: 4 
ISSN: 1548-0992


Abstract:
In the task of extracting association rules in datasets with numerical attributes, the usual approach is to previously discretize those attributes. In this paper, a deterministic method for the extraction of quantitative association rules is presented, which does not employ a previous discretization. It also tries to eliminate redundant rules and reduce the search. Experiments have been performed comparing it with the well-known deterministic algorithm Apriori, and statistical validation of the results was carried out using nonparametric tests. From the results obtained, the proposed approach can be seen as a suitable deterministic way of extracting quantitative association rules without a previous discretization.

Index Terms:
data mining, association rules, discretization   


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