High-utility-itemset mining, extended from frequent-itemset mining, considers external utilities of items in quantitative databases to obtain the itemsets with high utility. To easily understand those patterns with high utility values, fuzzy utility mining adopts fuzzy sets to increase the readability of the derived utility itemsets. However, real-world databases are usually dynamic. New transactions may be intermittently added, and the corresponding mined patterns must be updated to keep correct knowledge. In this paper, we propose an incremental method based on our previously proposed batch-processing fuzzy utility tree-based mining algorithm. The proposed approach adopts the fast-update (FUP) strategy. It considers newly coming data to readjust the head table and the tree structure, which generate desired itemsets in two phases. The experimental results reveal that the proposed algorithm outperforms the tree-based batch mining method.