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High-utility itemset mining finds itemsets from a transaction database withutility no less than a fixed user-defined threshold. The utility of an itemsetis defined as the sum of the utilities of its item. Several algorithms wereproposed to mine high-utility itemsets. However, no state-of-the-art algorithmperforms consistently good across dense and sparse datasets. In this paper, wepropose a novel data structure called Utility-Tree, and a tree-based algorithmcalled UT-Miner that mines high-utility itemsets in one-phase only withoutgenerating any candidates and uses a lightweight construction method to reducethe cost of creating projected databases during the search space exploration.The transaction information is stored compactly with every node of theUtility-Tree, and the information is computed efficiently during the recursiveinvocation of the algorithm. Experimental results on several real-life denseand sparse datasets reveal that UT-Miner is among the top-performing efficientalgorithms across different datasets.

A one-phase tree-based algorithm for mining high-utility itemsets from a transaction database