@inproceedings{bcc51dea756d46ef90e0f8e130c8ae59,
title = "A Declarative Framework for Mining Top-k High Utility Itemsets",
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{\textquoteright}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.",
keywords = "High utility, Propositional satisfiabilty, Top-k",
author = "Amel Hidouri and Said Jabbour and Badran Raddaoui and Mouna Chebbah and Yaghlane, \{Boutheina Ben\}",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 23rd International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2021 ; Conference date: 27-09-2021 Through 30-09-2021",
year = "2021",
month = jan,
day = "1",
doi = "10.1007/978-3-030-86534-4\_24",
language = "English",
isbn = "9783030865337",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "250--256",
editor = "Matteo Golfarelli and Robert Wrembel and Gabriele Kotsis and Tjoa, \{A Min\} and Ismail Khalil",
booktitle = "Big Data Analytics and Knowledge Discovery - 23rd International Conference, DaWaK 2021, Proceedings",
}