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dc.contributor.authorWu, Cheng-Weien_US
dc.contributor.authorFournier-Viger, Philippeen_US
dc.contributor.authorGu, Jia-Yuanen_US
dc.contributor.authorTseng, Vincent S.en_US
dc.date.accessioned2017-04-21T06:48:42Z-
dc.date.available2017-04-21T06:48:42Z-
dc.date.issued2015en_US
dc.identifier.isbn978-1-4673-9606-6en_US
dc.identifier.urihttp://hdl.handle.net/11536/135993-
dc.description.abstractHigh utility itemsets (HUIs) mining refers to discovering sets of items that not only co-occur but also carry high utilities (e.g., high profits). HUI mining receives extensive attentions in recent years due to the wide applications in various domains like commerce and biomedicine. However, huge number of HUls might be produced to users, which degrades the efficiency of the mining process. A promising solution to this problem is to mine closed(+) high utility itemset (CHUI), a compact and lossless representation of HUls. Nevertheless, existing algorithms incur the problem of producing a large amount of candidates, which degrades the mining performance in terms of time and space. In this paper, a novel algorithm named CHUI-Miner (Closed(+) High Utility ltemset mining without candidates) for mining CHUls is proposed, which directly computes the utility of itemsets without producing candidates. To our best knowledge, this is the first work addressing the issue of mining CHUls without candidate generation. Experimental results show that CHUl-Miner is several orders of magnitude faster than the state-of-the-art algorithms.en_US
dc.language.isoen_USen_US
dc.subjectutility miningen_US
dc.subjectclosed itemset miningen_US
dc.subjectclosed(+) high utility itemseten_US
dc.subjectcompact and lossless representationen_US
dc.titleMining Closed(+) High Utility Itemsets without Candidate Generationen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2015 CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE (TAAI)en_US
dc.citation.spage187en_US
dc.citation.epage194en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000380406200022en_US
dc.citation.woscount1en_US
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