Mining high-utility itemsets (HUIs) from a transaction database refers to the discovery of itemsets with high utilities like profits. Most of existing studies discover HUIs from a transaction database in two phases. In phase 1, different overestimation methods are applied to calculate the upper bounds of the utilities of itemsets. Since the overestimated utilities of itemsets are adopted, the itemsets whose overestimated utilities are no less than a user-specified threshold are selected as candidate HUIs, and they are verified by scanning the database one more time in phase 2. However, a large number of candidate HUIs incur two problems: 1) it requires excessive memory to store these candidates;2) it needs a large amount of running time to calculate their exact utilities. Vertical data format has been applied to mine HUIs recently. However this kind of method cannot deal with transactions with the same items effectively so that the size of database cannot be reduced su?ciently. The overall performance of algorithms is degraded consequently. Thus an algorithm HUITWU is proposed in this paper for mining HUIs. A novel data structure HUITWU-Tree is adopted to e?ciently calculate the utilities of itemsets in a database. Extensive studies with both sparse and dense datasets have demonstrated that our proposed algorithm is more than an order of magnitude faster and consumes less memory than the state-of-the-art algorithms.