Abstract
Among the useful tools for the retail stores to analyze their customer behaviors is through the task of mining high-utility itemset (HUIM), which is to reveal the combinations of items which offer high. However, most of them different abstraction levels of items. The CLH-Miner algorithm was presented to solve this problem. It adopts categorization of items with the HUIM to discover interesting itemsets not contained in traditional HUIM approaches. Whereas CLH-Miner discovers itemsets from different levels of abstraction efficiently, the algorithm is sequential. It cannot, therefore, use powerful, easily available, multi-core processors. This work tackles this drawback through the use of a parallel method called the pCLH-Miner algorithm to significantly reduce mining times. The algorithm proposes a way to split the search space into separate parts and assign them to each different core. The pCLH-miner is shown to high efficiency compared CLH-Miner by experiments on real-world databases.
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Tung, N.T., Nguyen, L.T.T., Nguyen, T.D.D., Kozierkiewicz, A. (2021). Cross-Level High-Utility Itemset Mining Using Multi-core Processing. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_35
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