Fast Utility Mining on Sequence Data
High-utility sequential pattern (HUSP) mining is an emerging topic in the field of knowledge discovery in databases. It consists of discovering subsequences that have a high utility (importance) in sequences, which can be referred to as HUSPs. HUSPs can be applied to many real-life applications, suc...
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| Veröffentlicht in: | IEEE transactions on cybernetics 2021-02, Vol.51 (2), p.487-500 |
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| container_title | IEEE transactions on cybernetics |
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| creator | Gan, Wensheng Lin, Jerry Chun-Wei Zhang, Jiexiong Fournier-Viger, Philippe Chao, Han-Chieh Yu, Philip S. |
| description | High-utility sequential pattern (HUSP) mining is an emerging topic in the field of knowledge discovery in databases. It consists of discovering subsequences that have a high utility (importance) in sequences, which can be referred to as HUSPs. HUSPs can be applied to many real-life applications, such as market basket analysis, e-commerce recommendations, click-stream analysis, and route planning. Several algorithms have been proposed to efficiently mine utility-based useful sequential patterns. However, due to the combinatorial explosion of the search space for low utility threshold and large-scale data, the performances of these algorithms are unsatisfactory in terms of runtime and memory usage. Hence, this article proposes an efficient algorithm for the task of HUSP mining, called HUSP mining with UL-list (HUSP-ULL). It utilizes a lexicographic q -sequence (LQS)-tree and a utility-linked (UL)-list structure to quickly discover HUSPs. Furthermore, two pruning strategies are introduced in HUSP-ULL to obtain tight upper bounds on the utility of the candidate sequences and reduce the search space by pruning unpromising candidates early. Substantial experiments on both real-life and synthetic datasets showed that HUSP-ULL can effectively and efficiently discover the complete set of HUSPs and that it outperforms the state-of-the-art algorithms. |
| doi_str_mv | 10.1109/TCYB.2020.2970176 |
| format | Article |
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It consists of discovering subsequences that have a high utility (importance) in sequences, which can be referred to as HUSPs. HUSPs can be applied to many real-life applications, such as market basket analysis, e-commerce recommendations, click-stream analysis, and route planning. Several algorithms have been proposed to efficiently mine utility-based useful sequential patterns. However, due to the combinatorial explosion of the search space for low utility threshold and large-scale data, the performances of these algorithms are unsatisfactory in terms of runtime and memory usage. Hence, this article proposes an efficient algorithm for the task of HUSP mining, called HUSP mining with UL-list (HUSP-ULL). It utilizes a lexicographic <inline-formula> <tex-math notation="LaTeX">q </tex-math></inline-formula>-sequence (LQS)-tree and a utility-linked (UL)-list structure to quickly discover HUSPs. Furthermore, two pruning strategies are introduced in HUSP-ULL to obtain tight upper bounds on the utility of the candidate sequences and reduce the search space by pruning unpromising candidates early. Substantial experiments on both real-life and synthetic datasets showed that HUSP-ULL can effectively and efficiently discover the complete set of HUSPs and that it outperforms the state-of-the-art algorithms.</description><identifier>ISSN: 2168-2267</identifier><identifier>EISSN: 2168-2275</identifier><identifier>DOI: 10.1109/TCYB.2020.2970176</identifier><identifier>PMID: 32142464</identifier><identifier>CODEN: ITCEB8</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Algorithms ; Combinatorial analysis ; Computer science ; Cybernetics ; Data mining ; Economic behavior ; Gallium nitride ; Itemsets ; linked-list structure ; Mining ; Route planning ; sequence ; Upper bound ; Upper bounds ; utility mining ; utility theory</subject><ispartof>IEEE transactions on cybernetics, 2021-02, Vol.51 (2), p.487-500</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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Furthermore, two pruning strategies are introduced in HUSP-ULL to obtain tight upper bounds on the utility of the candidate sequences and reduce the search space by pruning unpromising candidates early. 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| subjects | Algorithms Combinatorial analysis Computer science Cybernetics Data mining Economic behavior Gallium nitride Itemsets linked-list structure Mining Route planning sequence Upper bound Upper bounds utility mining utility theory |
| title | Fast Utility Mining on Sequence Data |
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