Mining high utility itemsets based on pattern growth without candidate generation

Abstract

Mining high utility itemsets (HUIs) has been an active research topic in data mining in recent years. Existing HUI mining algorithms typically take two steps: generating candidates and identifying utility values of these candidate itemsets. The performance of these algorithms depends on the efficiency of both steps, both of which are usually time-consuming. In this study, we propose an efficient pattern-growth based HUI mining algorithm, called tail-node tree-based high-utility itemset (TNT-HUI) mining. This algorithm avoids the time-consuming candidate generation step, as well as the need of scanning the original dataset multiple times for exact utility values, as supported by a novel tree structure, named the tail-node tree (TN-Tree). The performance of TNT-HUI was evaluated in comparison with state-of-the-art benchmark methods on different datasets. Experimental results showed that TNT-HUI outperformed benchmark algorithms in both execution time and memory use by orders of magnitude. The performance gap is larger for denser datasets and lower thresholds.