Fuzzy itemset mining was previously proposed to consider the quantity of items and derive linguistic rules that are simple and more comprehensible to decision makers. However, most existing fuzzy mining techniques adopt Apriori-based techniques to deal with the problem of mining fuzzy frequent itemsets, and thus their execution efficiency is not good. In this paper, we thus propose an efficient mining approach to speed up the efficiency of finding fuzzy frequent itemsets from databases. In particular, a data-reduction strategy is designed to effectively help prune unpromising fuzzy terms in transactions at each pass in comparison with the other existing algorithms. Through a series of experimental evaluations, the results reveal that the proposed approach runs
faster than the existing fuzzy mining algorithms on several synthetic and real datasets under different parameter settings.