Extending knowledge based redundancy in association rules with imprecise knowledge

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 set of rules; so the analysis capability, from the user point of view, is diminishing. Hence, it is difficult to use the found model in order to assist decision-making process. The previous handicap is heightened in presence of redundant rules in the final set. In this work we study a way to eliminate redundancy in association rules with uncertainty, with imprecise user prior knowledge. A post-processing method is developed to eliminate this kind of redundancy, using association rules known by the user. Our proposal allows to find more compact models of association rules to ease its use in the decision-making process. The developed experiments have shown that the reduction using certainty factor has a slightly better behavior. The most important contribution of this paper is the definition of a mechanism to remove knowledge based redundancy using Dempster-Schaffer Theory and the Certainty Factor Model