In order to improve the efficiency of building Fuzzy Associative Classifier (FAC) on the dynamic data sets, an incremental fuzzy associative classification method based on eVQ (evolving Vector Quantization) clustering algorithm was proposed. Firstly, eVQ clustering algorithm was adopted to incrementally update the parameters of Gauss membership functions of quantitative attributes. Secondly, Update With Early Pruning (UWEP) algorithm was extended to incrementally mine fuzzy frequent itemsets. Finally, Fuzzy CORRelation (FCORR) of Fuzzy Associative Classification Rule (FACR) and the length of antecedent of FACR were regarded as measures to prune and update fuzzy associative classification rule base. The experimental results on four UCI benchmark data sets show that compared with the batch fuzzy association classification modeling method, the proposed method can reduce the time of training the FAC in the premise of not decreasing the accuracy and interpretability. The Gauss membership function updating method based on eVQ clustering algorithm contributes to improve the classification accuracy of the FAC on the dynamic data sets.