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dc.contributor.authorWang, Jun-Zheen_US
dc.contributor.authorYang, Zong-Huaen_US
dc.contributor.authorHuang, Jiun-Longen_US
dc.date.accessioned2014-12-08T15:36:53Z-
dc.date.available2014-12-08T15:36:53Z-
dc.date.issued2014-01-01en_US
dc.identifier.isbn978-94-017-8798-7; 978-94-017-8797-0en_US
dc.identifier.issn1876-1100en_US
dc.identifier.urihttp://dx.doi.org/10.1007/978-94-017-8798-7_7en_US
dc.identifier.urihttp://hdl.handle.net/11536/25286-
dc.description.abstractHigh utility sequential pattern mining is to mine sequences with high utility (e. g. profits) but probably with low frequency. In some applications such as marketing analysis, high utility sequential patterns are usually more useful than sequential patterns with high frequency. In this paper, we devise two pruning strategies RSU and PDU, and propose HUS-Span algorithm based on these two pruning strategies to efficiently identify high utility sequential patterns. Experimental results show that HUS-Span algorithm outperforms prior algorithms by pruning more low utility sequences.en_US
dc.language.isoen_USen_US
dc.titleAn Efficient Algorithm for High Utility Sequential Pattern Miningen_US
dc.typeProceedings Paperen_US
dc.identifier.doi10.1007/978-94-017-8798-7_7en_US
dc.identifier.journalFRONTIER AND INNOVATION IN FUTURE COMPUTING AND COMMUNICATIONSen_US
dc.citation.volume301en_US
dc.citation.issueen_US
dc.citation.spage49en_US
dc.citation.epage56en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000342931300007-
Appears in Collections:Conferences Paper