Abstract In this paper, we define the problem of fuzzy close frequent itemset mining to discover the rules of the data. A concise tree-based data synoposis named FCTree is built, where the fuzzy itemsets are sorted by their supports. In addition, an algorithm called FCFIMiner is proposed to construct and maintain the FCTree . We conduct superset pruning from the result of the FCTree . The experimental works over 2 databases show the proposed algorithm has a much better performance.