Hailye Tekleselase
High Utility Itemset Mining (HUIM) from large transaction databases has garnered significant attention as it accounts for the revenue of the items purchased in a transaction. While most tree-based algorithms to mine HUIs transform the database to an item-prefix tree, they discard the unpromising items and consume a significant amount of memory. Employing trees that store transaction-level information has proven to enhance the mining process in conjunction with such prefix trees. In this regard, the present work proposes memory-efficient trees namely- Utility Prime Tree (UPT), Prime Cantor Function Tree (PCFT), and String based Utility Prime Tree (SUPT) that encode entire transaction information in a node, unlike the prefix-based trees through a single database scan. Experiments conducted on both the real and synthetic datasets show that these structures consume significantly less memory when compared to the tree structures in the literature.
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