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EHMIN: Efficient approach of list based high-utility pattern mining with negative unit profits

Kim Department of Computer Engineering, Sejong University, Seoul, South Korea|
Unil (8958234600) | Jerry (56825962600); Yun | Bay (35147075900); Chun-Wei Lin | Eunchul (8381613200); Vo | Hyeonmo (57715019900); Yoon | Chanhee (57396765200); Kim Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway| Taewoong (57223425628); Lee Faculty of Information Technology, HUTECH University, Ho Chi Minh City, Viet Nam| Heonho (57211337296); Ryu Department of Electronics Engineering, Konkuk University, Seoul, South Korea|

Expert Systems with Applications Số , năm 2022 (Tập 209, trang -)

ISSN: 9574174

ISSN: 9574174

DOI: 10.1016/j.eswa.2022.118214

Tài liệu thuộc danh mục:



Từ khóa: Profitability; Algorithm performance; High-utility pattern mining; Large database; List-based data structure; Negative unit profit; Pattern information; Pattern mining; Real-world; Traditional approaches; Useful patterns; Data mining
Tóm tắt tiếng anh
High-utility pattern mining is an important sub-literature in the data mining literature. This literature discusses the discovery of useful pattern information from large databases by considering not only supports of patterns but also profits and quantities of items. This literature has the potential to be applied to various problems in the real world, so many methods for the improvement of the algorithm performance have been studied. Moreover, there have also been attempts to extend the flexibility of this literature. The traditional approaches in this literature considered the positive unit profits of items in a given database only. However, this literature can take extended flexibility into account by considering negative as well as positive unit profits of the items. In this paper, we suggest an efficient approach for mining high-utility patterns with negative unit profits. Moreover, the experimental performance tests, which are performed on various real and synthetic datasets in this paper, show that the proposed algorithm has a better performance than the state-of-the-art methods in this literature in terms of the runtime, memory usage, and scalability. � 2022 Elsevier Ltd

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