Implication-based fuzzy association rules

Abstract

Fuzzy association rules provide a data miningt ool which is especially interestingfrom a knowledge-representational point of view since fuzzy attribute values allow for expressingrul es in terms of natural language. In this paper, we show that fuzzy associations can be interpreted in different ways and that the interpretation has a strong influence on their assessment and, hence, on the process of rule mining. We motivate the use of multiple-valued implication operators in order to model fuzzy association rules and propose quality measures suitable for this type of rule. Moreover, we introduce a semantic model of fuzzy association rules which suggests to consider them as a convex combination of simple association rules. This model provides a sound theoretical basis and gives an explicit meaning to fuzzy associations. Particularly, the aforementioned quality measures can be justified within this framework.