Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits.Although a number of relevant algorithms have been proposed in recent years, they incur the problem of producing a large number ofcandidate itemsets for high utility itemsets. Such a large number of candidate itemsets degrades the mining performance in terms ofexecution time and space requirement. The situation may become worse when the database contains lots of long transactions or longhigh utility itemsets. In this paper, we propose two algorithms, namelyutility pattern growth(UP-Growth) and UP-Growth+, for mininghigh utility itemsets with a set of effective strategies for pruning candidate itemsets. The information of high utility itemsets ismaintained in a tree-based data structure namedutility pattern tree(UP-Tree) such that candidate itemsets can be generatedefficiently with only two scans of database. The performance of UP-Growth and UP-Growth+is compared with the state-of-the-artalgorithms on many types of both real and synthetic data sets. Experimental results show that the proposed algorithms, especially UP-Growth+, not only reduce the number of candidates effectively but also outperform other algorithms substantially in terms of runtime,especially when databases contain lots of long transactions. Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases