Title | An efficient utility-list based high-utility itemset mining algorithm |
Author | |
Corresponding Author | Fang, Wei |
Publication Years | 2022-07-01
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DOI | |
Source Title | |
ISSN | 0924-669X
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EISSN | 1573-7497
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Volume | 53Issue:6Pages:6992-7006 |
Abstract | High-utility itemset mining (HUIM) is an important task in data mining that can retrieve more meaningful and useful patterns for decision-making. One-phase HUIM algorithms based on the utility-list structure have been shown to be the most efficient as they can mine high-utility itemsets (HUIs) without generating candidates. However, storing itemset information for the utility-list is time-consuming and memory consuming. To address this problem, we propose an efficient simplified utility-list-based HUIM algorithm (HUIM-SU). In the proposed HUIM-SU algorithm, the simplified utility-list is proposed to obtain all HUIs effectively and reduce memory usage in the depth-first search process. Based on the the simplified utility-list, repeated pruning according to the transaction-weighted utilisation (TWU) reduces the number of items. In addition, a construction tree and compressed storage are introduced to further reduce the search space and the memory usage. The extension utility and itemset TWU are then proposed to be the upper bounds, which reduce the search space considerably. Extensive experimental results on dense and sparse datasets indicate that the proposed HUIM-SU algorithm is highly efficient in terms of the number of candidates, memory usage, and execution time. |
Keywords | |
URL | [Source Record] |
Indexed By | |
Language | English
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SUSTech Authorship | Others
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Funding Project | National Key R&D Program of China["2017YFC1601000","2017YFC1601800"]
; National Natural Science foundation of China["62073155","62106088","61673194","61672263"]
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WOS Research Area | Computer Science
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WOS Subject | Computer Science, Artificial Intelligence
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WOS Accession No | WOS:000824373500003
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Publisher | |
EI Accession Number | 20222912386067
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EI Keywords | Decision making
; Digital storage
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ESI Classification Code | Data Storage, Equipment and Techniques:722.1
; Data Processing and Image Processing:723.2
; Management:912.2
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ESI Research Field | ENGINEERING
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Scopus EID | 2-s2.0-85134351417
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Data Source | Web of Science
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Citation statistics |
Cited Times [WOS]:2
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Document Type | Journal Article |
Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/356177 |
Department | Department of Computer Science and Engineering |
Affiliation | 1.Wuxi Inst Technol, Sch Internet Things, Gaolang Rd, Wuxi 214121, Jiangsu, Peoples R China 2.Jiangnan Univ, Jiangsu Prov Engn Lab Pattern Recognit & Computat, Lihu Ave, Wuxi 214122, Jiangsu, Peoples R China 3.Western Norway Univ Appl Sci, Dept Comp Sci Elect Engn & Math Sci, Bergen, Norway 4.Southern Univ Sci & Technol, Comp Sci & Engn Dept, Shenzhen, Peoples R China |
Recommended Citation GB/T 7714 |
Cheng, Zaihe,Fang, Wei,Shen, Wei,et al. An efficient utility-list based high-utility itemset mining algorithm[J]. APPLIED INTELLIGENCE,2022,53(6):6992-7006.
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APA |
Cheng, Zaihe,Fang, Wei,Shen, Wei,Lin, Jerry Chun-Wei,&Yuan, Bo.(2022).An efficient utility-list based high-utility itemset mining algorithm.APPLIED INTELLIGENCE,53(6),6992-7006.
|
MLA |
Cheng, Zaihe,et al."An efficient utility-list based high-utility itemset mining algorithm".APPLIED INTELLIGENCE 53.6(2022):6992-7006.
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