On fuzzy association rules based on fuzzy cardinalities

Patrick Bosc (1) , Didier Dubois (2) , Henri Prade (2) , Olivier Pivert (1)

1 IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires
2 IRIT-ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage

The paper discusses the benefit of using fuzzy sets in data summaries based on generalized association rules. Fuzzy sets provide a convenient interface between labels and data and allow for partial belonging to connex but distinct classes. They thus offer a robust reading of the data. Starting with fuzzy partitions of attribute domains which are meaningful for a user, a procedure is described which enables data summaries involving fuzzy quantifiers to be built, by computing fuzzy cardinalities. The differnce between this new type of fuzzy summary and previous proposals is also pointed out.

Mots clés: Pattern matching; Possibility theory; Multidimensional systems; Fuzzy set theory; Medical diagnostic imaging; Proposals; Knowledge representation; Fault detection

Domaines: Intelligence artificielle [cs.AI]

Patrick Bosc, Didier Dubois, Henri Prade, Olivier Pivert. On fuzzy association rules based on fuzzy cardinalities. 10th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2001), Dec 2001, Melbourne, Australia. ⟨10.1109/FUZZ.2001.1007348⟩. ⟨hal-03394654⟩