完整後設資料紀錄
DC 欄位語言
dc.contributor.authorFournier-Viger, Philippeen_US
dc.contributor.authorLin, Jerry Chun-Weien_US
dc.contributor.authorWu, Cheng-Weien_US
dc.contributor.authorTseng, Vincent S.en_US
dc.contributor.authorFaghihi, Usefen_US
dc.date.accessioned2017-04-21T06:49:25Z-
dc.date.available2017-04-21T06:49:25Z-
dc.date.issued2016en_US
dc.identifier.isbn978-3-319-44403-1en_US
dc.identifier.isbn978-3-319-44402-4en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-319-44403-1_6en_US
dc.identifier.urihttp://hdl.handle.net/11536/136436-
dc.description.abstractMining high-utility itemsets (HUIs) is a key data mining task. It consists of discovering groups of items that yield a high profit in transaction databases. A major drawback of traditional high-utility itemset mining algorithms is that they can return a large number of HUIs. Analyzing a large result set can be very time-consuming for users. To address this issue, concise representations of high-utility itemsets have been proposed such as closed HUIs, maximal HUIs and generators of HUIs. In this paper, we explore a novel representation called the minimal high utility itemsets (MinHUIs), defined as the smallest sets of items that generate a high profit, study its properties, and design an efficient algorithm named MinFHM to discover it. An extensive experimental study with real-life datasets shows that mining MinHUIs can be much faster than mining other concise representations or all HUIs, and that it can greatly reduce the size of the result set presented to the user.en_US
dc.language.isoen_USen_US
dc.subjectUtility miningen_US
dc.subjectHigh-utility itemsetsen_US
dc.subjectMinimal itemsetsen_US
dc.titleMining Minimal High-Utility Itemsetsen_US
dc.typeProceedings Paperen_US
dc.identifier.doi10.1007/978-3-319-44403-1_6en_US
dc.identifier.journalDATABASE AND EXPERT SYSTEMS APPLICATIONS, DEXA 2016, PT Ien_US
dc.citation.volume9827en_US
dc.citation.spage88en_US
dc.citation.epage101en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000389020100006en_US
dc.citation.woscount0en_US
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