NARFO algorithm: Mining non-redundant and generalized association rules based on fuzzy ontologies

Abstract

Traditional approaches for mining generalized association rules are based only on database contents, and focus on exact matches among items. However, in many applications, the use of some background knowledge, as ontologies, can enhance the discovery process and generate semantically richer rules. In this way, this paper proposes the NARFO algorithm, a new algorithm for mining non-redundant and generalized association rules based on fuzzy ontologies. Fuzzy ontology is used as background knowledge, to support the discovery process and the generation of rules. One contribution of this work is the generalization of non-frequent itemsets that helps to extract important and meaningful knowledge. NARFO algorithm also contributes at post-processing stage with its generalization and redundancy treatment. Our experiments showed that the number of rules had been reduced considerably, without redundancy, obtaining 63.63% average reduction in comparison with XSSDM algorithm. © 2009 Springer Berlin Heidelberg.