Abstract
Many algorithms have been proposed to find high utility itemsets (sets of items that yield a high profit) in customer transactions. Though, it is useful to analyze customer behavior, it ignores information about item categories. To consider a product taxonomy and find high utility itemsets describing relationships between items and categories, the ML-HUI Miner was recently proposed. But it cannot find cross-level itemsets (itemsets mixing items from different taxonomy levels), and it is inefficient as it does not use relationships between categories to reduce the search space. This paper addresses these issues by proposing a novel problem called cross-level high utility itemset mining, and an algorithm named CLH-Miner. It relies on novel upper bounds to efficiently search for high utility itemsets when considering a taxonomy. An experimental evaluation with real retail data shows that the algorithm is efficient and can discover insightful patterns describing customer purchases.
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Fournier-Viger, P., Wang, Y., Lin, J.CW., Luna, J.M., Ventura, S. (2020). Mining Cross-Level High Utility Itemsets. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds) Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. IEA/AIE 2020. Lecture Notes in Computer Science(), vol 12144. Springer, Cham. https://doi.org/10.1007/978-3-030-55789-8_73
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