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dc.contributor.authorLi, Hlia-Fuen_US
dc.contributor.authorHo, Chin-Chuanen_US
dc.contributor.authorLee, Suh-Yinen_US
dc.date.accessioned2014-12-08T15:09:51Z-
dc.date.available2014-12-08T15:09:51Z-
dc.date.issued2009-03-01en_US
dc.identifier.issn0957-4174en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.eswa.2007.12.054en_US
dc.identifier.urihttp://hdl.handle.net/11536/7541-
dc.description.abstractOnline mining of closed frequent itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we propose an efficient one-pass algorithm, NewMoment to maintain the set of closed frequent itemsets in data streams with a transaction-sensitive sliding window. An effective bit-sequence representation of items is used in the proposed algorithm to reduce the time and memory needed to slide the windows. Experiments show that the proposed algorithm not only attain highly accurate mining results. but also run significant faster and consume less memory than existing algorithm Moment for mining closed frequent itemsets over recent data streams (C) 2007 Elsevier Ltd. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectData miningen_US
dc.subjectData streamsen_US
dc.subjectClosed frequent itemsetsen_US
dc.subjectSingle-pass miningen_US
dc.subjectIncremental updateen_US
dc.titleIncremental updates of closed frequent itemsets over continuous data streamsen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.eswa.2007.12.054en_US
dc.identifier.journalEXPERT SYSTEMS WITH APPLICATIONSen_US
dc.citation.volume36en_US
dc.citation.issue2en_US
dc.citation.spage2451en_US
dc.citation.epage2458en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000262178000143-
dc.citation.woscount18-
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