Journal
KNOWLEDGE-BASED SYSTEMS
Volume 248, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2022.108865
Keywords
Pattern mining; High utility itemset; Utility list buffer; Bitwise operations
Categories
Funding
- Science and Technology Planning Project of Sichuan Province, China [2021YFS0391]
- Scientific Research Project of State Grid Sichuan Electric Power Company Information and Communica-tion Company, China [SGSCXT00XGJS2100116]
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High utility itemset mining is a key problem in data mining, aiming to find itemsets with high importance or profit in a database for decision-making support. This paper proposes to improve the efficiency of utility-list construction through a set of bitwise operations and a new data structure, leading to faster mining of high utility itemsets.
HUIM (High utility itemset mining) is a key problem in data mining. The goal is to find itemsets having a high importance or profit in a database, to identify useful knowledge that can support decision making. In recent years, many HUIM algorithms have been put forward. Among them, utility-list-based algorithms have become very popular as they are easily extendable and efficient. Although several improvements were made, efficiency remains a critical issue. To address this problem, this paper proposes to improve the utility-list construction process, a key operation that has not been much studied in prior work. A novel set of bitwise operations is proposed called BEO (Bit mErge cOnstruction) to speed up the construction process. Besides, a novel data structure called UBP (Utility Bit Partition) is designed to support BEO. This structure is integrated into a novel UBP-Miner algorithm, which also applies several search space reduction strategies. Experimental results show that UBP-Miner is faster than several state-of-the-art algorithms such as HUI-Miner* and ULB-Miner on common benchmark datasets.(C) 2022 Elsevier B.V. All rights reserved.
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