A Discussion of Indices for the Evaluation of Fuzzy Associations in Relational Databases
Find this in a Library
This paper investigates techniques to identify and evaluate associations in a relational database that are expressed by fuzzy if-then rules. Extensions of the classical confidence measure based on the α-cut decompositions of the fuzzy sets are proposed to address the problems associated with the normalization in scalar-valued generalizations of confidence. An analysis by α-level differentiates strongly and weakly supported associations and identifies robustness in an association. In addition, a method is proposed to assess the validity of a fuzzy association based on the ratio of examples to counterexamples.
& Sudkamp, T.
(2003). A Discussion of Indices for the Evaluation of Fuzzy Associations in Relational Databases. Fuzzy Sets and Systems — IFSA 2003. IFSA 2003. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence), 2715, 111-118.