Abstract
High-utility itemset (HUI) mining is a popular data mining task, consisting of enumerating all groups of items that yield a high profit in a customer transaction database. However, an important issue with traditional HUI mining algorithms is that they tend to find itemsets having many items. But those itemsets are often rare, and thus may be less interesting than smaller itemsets for users. In this paper, we address this issue by presenting a novel algorithm named FHM\(+\) for mining HUIs, while considering length constraints. To discover HUIs efficiently with length constraints, FHM\(+\) introduces the concept of Length Upper-Bound Reduction (LUR), and two novel upper-bounds on the utility of itemsets. An extensive experimental evaluation shows that length constraints are effective at reducing the number of patterns, and the novel upper-bounds can greatly decrease the execution time, and memory usage for HUI mining.
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Fournier-Viger, P., Lin, J.CW., Duong, QH., Dam, TL. (2016). FHM\(+\): Faster High-Utility Itemset Mining Using Length Upper-Bound Reduction. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_11
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DOI: https://doi.org/10.1007/978-3-319-42007-3_11
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