A quantitative algorithm for extracting generic basis of fuzzy association rules

Fuzzy association rules (FAR) are well suited for the thinking of human subject and will help to increase the flexibility for supporting user in making decisions or designing the fuzzy systems. The classic extraction of associative rules suffers from a high number of generated rules. To overcome this problem, several studies have been developed to extract a generic subset of all rules. These generic databases are particularly suitable for dense contexts. The fuzzy contexts are highly dense contexts. So it is necessary to define a generic basis for all FAR. In this paper, we present a new algorithm for extracting FAR based on the extraction of generic basis of these last. This generic basis constitutes a compact nucleus of FAR which is based on the extraction of fuzzy closed itemsets and their corresponding fuzzy minimal generators.