On efficiently mining high utility sequential patterns

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题名:	On efficiently mining high utility sequential patterns
作者:	陳以錚;Wang, Jun-Zhe;Huang, Jiun-Long;Chen, Yi-Cheng
贡献者:	管理學院資訊管理學系
关键词:	Algorithms;Analysis;Candidates;Computer Science;Data mining;Data Mining and Knowledge Discovery;Database Management;Efficiency;Information Storage and Retrieval;Information Systems and Communication Service;Information Systems Applications (incl.Internet);IT in Business;Pattern analysis;Pattern recognition;Performance evaluation;Pruning;Regular Paper;Searching;Strategy;Studies;Television sets;Utilities
日期:	2016-11-01
上传时间:	2026-04-23 13:47:47 (UTC+8)
出版者:	Springer London;London: Springer Science and Business Media LLC
摘要:	摘要： High utility sequential pattern mining is an emerging topic in pattern mining, which refers to identify sequences with high utilities (e.g., profits) but probably with low frequencies. To identify high utility sequential patterns, due to lack of downward closure property in this problem, most existing algorithms first generate candidate sequences with high sequence-weighted utilities (SWUs), which is an upper bound of the utilities of a sequence and all its supersequences, and then calculate the actual utilities of these candidates. This causes a large number of candidates since SWU is usually much larger than the real utilities of a sequence and all its supersequences. In view of this, we propose two tight utility upper bounds, prefix extension utility and reduced sequence utility, as well as two companion pruning strategies, and devise HUS-Span algorithm to identify high utility sequential patterns by employing these two pruning strategies. In addition, since setting a proper utility threshold is usually difficult for users, we also propose algorithm TKHUS-Span to identify top- k high utility sequential patterns by using these two pruning strategies. Three searching strategies, guided depth-first search (GDFS), best-first search (BFS) and hybrid search of BFS and GDFS, are also proposed to improve the efficiency of TKHUS-Span. Experimental results on some real and synthetic datasets show that HUS-Span and TKHUS-Span with strategy BFS are able to generate less candidate sequences and thus outperform other prior algorithms in terms of mining efficiency. 其他題名： Knowl Inf Syst 出版者： London: Springer Science and Business Media LLC 出版日期： 2016-11-01 出處： Knowledge and Information Systems, 2016-11, Vol.49 (2), p.597-627 資源來源： ABI/INFORM Collection 版權： Springer-Verlag London 2016 識別號： ISSN: 0219-1377 識別號： EISSN: 0219-3116 識別號： DOI: 10.1007/s10115-015-0914-8 識別號： CODEN: KISNCR
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