Abstract
The problem of mining high utility itemsets entails identifying a set of items that yield the highest utility values based on a given user utility threshold. In this paper, we utilize propositional satisfiability to model the Top-k high utility itemset problem as the computation of models of CNF formulas. To achieve our goal, we use a decomposition technique to improve our method’s scalability by deriving small and independent sub-problems to capture the Top-k high utility itemsets. Through empirical evaluations, we demonstrate that our approach is competitive to the state-of-the-art specialized algorithms.
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Hidouri, A., Jabbour, S., Raddaoui, B., Chebbah, M., Yaghlane, B.B. (2021). A Declarative Framework for Mining Top-k High Utility Itemsets. In: Golfarelli, M., Wrembel, R., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2021. Lecture Notes in Computer Science(), vol 12925. Springer, Cham. https://doi.org/10.1007/978-3-030-86534-4_24
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DOI: https://doi.org/10.1007/978-3-030-86534-4_24
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