In order to treat and analyze real datasets, fuzzy association rules have been proposed. Several algorithms have been introduced to extract these rules. However, these algorithms suffer from the problems of utility, redundancy and large number of extracted fuzzy association rules. The expert will then be confronted with this huge amount of fuzzy association rules. The task of validation becomes fastidious. In order to solve these problems, we propose a new validation method. Our method is based on three steps. (i) We extract a generic base of non redundant fuzzy association rules by applying EFAR-PN algorithm based on fuzzy formal concept analysis. (ii) we categorize extracted rules into groups and (iii) we evaluate the relevance of these rules using structural equation model.