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- W3203031103 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." @default.
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- W3203031103 date "2021-01-01" @default.
- W3203031103 modified "2023-10-16" @default.
- W3203031103 title "Cross-Level High-Utility Itemset Mining Using Multi-core Processing" @default.
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- W3203031103 doi "https://doi.org/10.1007/978-3-030-88081-1_35" @default.
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