A Systematic Approach to the Assessment of Fuzzy Association Rules∗ Draft of a paper accepted for publication in the journal Data Mining and Knowledge Discovery

In order to allow for the analysis of data sets including numerical attributes, several generalizations of association rule mining based on fuzzy sets have been proposed in the literature. While the formal specification of fuzzy associations is more or less straightforward, the assessment of such rules by means of appropriate quality measures is less obvious. Particularly, it assumes an understanding of the semantic meaning of a fuzzy rule. This aspect has been ignored by most existing proposals, which must therefore be considered as ad-hoc to some extent. In this paper, we develop a systematic approach to the assessment of fuzzy association rules. To this end, we proceed from the idea of partitioning the data stored in a database into examples of a given ∗This article is a revised and extended version of a paper presented at the 10th International Fuzzy Systems Association World Congress, Istambul, 2003 [25].