Recently, mining useful information and knowledge from transactions is evolving into an important research issue. Many algorithms have thus been proposed for mining association rules based on items with binary values. Transactions with quantitative values are, however, also commonly seen in real-world applications. The fuzzy frequent-pattern tree (FP tree) algorithm has been proposed for extracting fuzzy frequent itemsets from quantitative transactions. Only the term with the maximum cardinality in later processes is used, making the number of fuzzy regions processed equal to the number of original items, which reduces the processing time. In real world applications, however, multiple fuzzy regions of an item produce better fuzzy association rules than those obtained using a single region. In this paper, the multiple fuzzy FP tree (MFFP tree) algorithm is proposed for mining fuzzy frequent itemsets from transactions with quantitative values. When extending the FP-tree structure to handle fuzzy data, the processing becomes much more complex than that for the original FP-tree structure since the fuzzy intersection in each transaction has to be handled. The MFFP-tree construction algorithm is designed and the MFFP-growth mining approach is proposed for mining the fuzzy frequent itemsets from the tree structure. Experiments were conducted to evaluate the performance of the proposed approach.