Abstract

DIAZ VERA, Julio; MOLINA FERNANDEZ, Carlos  and  VILA MIRANDA, María- Amparo. Redundancy Reduction in Association Rules. Rev cuba cienc informat [online]. 2016, vol.10, n.1, pp. 55-70. ISSN 2227-1899.

ABSTRACT Association Rules Mining is one of the most studied and widely applied fields in Data Mining. However, the Discovery models usually result in a very large sets of rules; so the analysis capability, from a user point of view, are dismissing. It is difficult to use the found model in order to help the decision-making process. The previous handicap is accentuated in presence of redundant rules in the final set. In this work a new definition of redundancy in association rules is proposed, based in user’s prior knowledge. A post-processing method to eliminate this kind of redundancy, using association rules known by user is developed. Our proposal allows to find more compact models of association rules to facilitate its use in the decision-making process. The developed experiments have shown reduction levels that exceed 90% of all generated rules, using prior knowledge always below 10%. So our method improves the efficiency of association rules mining and the utilization of discovered association rules.

Keywords : Association Rule Mining; Redundant Rules; Post-processing of association rules.

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