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dc.contributor.authorHo, Chin-Chuanen_US
dc.contributor.authorLi, Hua-Fuen_US
dc.contributor.authorKuo, Fang-Feien_US
dc.contributor.authorLee, Suh-Yinen_US
dc.date.accessioned2014-12-08T15:24:38Z-
dc.date.available2014-12-08T15:24:38Z-
dc.date.issued2006en_US
dc.identifier.isbn978-0-7695-2702-4en_US
dc.identifier.urihttp://hdl.handle.net/11536/17112-
dc.description.abstractIncremental mining of sequential patterns from data streams is one of the most challenging problems in mining data streams. However, previous work of mining sequential patterns from data streams is almost focused on mining of patterns from stream of item-sequences, not stream of itemset-sequences. In this paper, we propose an efficient single-pass algorithm, called IncSPAM, to maintain the set of sequential patterns from itemset-sequence 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 IncSPAM algorithm is efficient for mining sequential patterns over data streams.en_US
dc.language.isoen_USen_US
dc.titleIncremental mining of sequential patterns over a stream sliding windowen_US
dc.typeProceedings Paperen_US
dc.identifier.journalICDM 2006: Sixth IEEE International Conference on Data Mining, Workshopsen_US
dc.citation.spage677en_US
dc.citation.epage681en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000245603100124-
Appears in Collections:Conferences Paper