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dc.contributor.authorChiu, Shih-Chuanen_US
dc.contributor.authorLi, Hua-Fuen_US
dc.contributor.authorHuang, Jiun-Longen_US
dc.contributor.authorYou, Hsin-Hanen_US
dc.date.accessioned2014-12-08T15:11:48Z-
dc.date.available2014-12-08T15:11:48Z-
dc.date.issued2011-04-01en_US
dc.identifier.issn0165-5515en_US
dc.identifier.urihttp://dx.doi.org/10.1177/0165551511401539en_US
dc.identifier.urihttp://hdl.handle.net/11536/9048-
dc.description.abstractMining inter-transaction association rules is one of the most interesting issues in data mining research. However, in a data stream environment the previous approaches are unable to find the result of the new-incoming data and the original database without re-computing the whole database. In this paper, we propose an incremental mining algorithm, called DSM-CITI (Data Stream Mining for Closed Inter-Transaction Itemsets), for discovering the set of all frequent inter-transaction itemsets from data streams. In the framework of DSM-CITI, a new in-memory summary data structure, ITP-tree, is developed to maintain frequent inter-transaction itemsets. Moreover, algorithm DSM-CITI is able to construct ITP-tree incrementally and uses the property to avoid unnecessary updates. Experimental studies show that the proposed algorithm is efficient and scalable for mining frequent inter-transaction itemsets over stream sliding windows.en_US
dc.language.isoen_USen_US
dc.subjectdata miningen_US
dc.subjectdata streamsen_US
dc.subjectincremental miningen_US
dc.subjectstream sliding window miningen_US
dc.subjectfrequent inter-transaction itemsetsen_US
dc.titleIncremental mining of closed inter-transaction itemsets over data stream sliding windowsen_US
dc.typeArticleen_US
dc.identifier.doi10.1177/0165551511401539en_US
dc.identifier.journalJOURNAL OF INFORMATION SCIENCEen_US
dc.citation.volume37en_US
dc.citation.issue2en_US
dc.citation.spage208en_US
dc.citation.epage220en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000289408600008-
dc.citation.woscount2-
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