Mining Generalized Association Rules using Fuzzy Ontologies with Context-based Similarity

Abstract

In crisp contexts taxonomies are used in different steps of the mining process. When the objective is the generalization they are used, manly, in the pre-processing or post-processing stages. On the other hand, in fuzzy contexts, fuzzy taxonomies are used, mainly, in the pre-processing step, during the generation of extended transactions. A great problem of such transactions is related to the generation of huge amount of candidates and rules. Beyond that, the inclusion of ancestors in the same ends up generating problems of redundancy. Besides, it is possible to see that many works have directed efforts for the question of mining fuzzy rules, exploring linguistic terms, but few approaches have proposed new steps of the mining process. In this sense, this paper propose the Context FOntGAR algorithm, a new algorithm for mining generalized association rules under all levels of fuzzy ontologies composed by specialization/generalization degrees varying in the interval [0,1]. In order to obtain more semantic enrichment, the rules may be composed by similarity relations, which are represented at the fuzzy ontologies in different contexts. In this work the generalization is done during the post-processing step. Other relevant points are the specification of a generalization approach; including a grouping rules treatment, and an efficient way of calculating both support and confidence of generalized rules during this step.