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dc.contributor.authorTan, H.
dc.contributor.authorDillon, Tharam S.
dc.contributor.authorHadzic, Fedja
dc.contributor.authorChang, Elizabeth
dc.date.accessioned2017-01-30T13:34:31Z
dc.date.available2017-01-30T13:34:31Z
dc.date.created2008-11-12T23:32:20Z
dc.date.issued2006
dc.identifier.citationTan, Henry and Dillon, Tharam and Hadzic, Fedja and Chang, Elizabeth. 2006. : SEQUEST: Mining frequent subsequences using DMA strips, in Zanasi, A. and Temis, S.A. and Brebbia, C.A. and Ebecken, N.F.F. (ed), Seventh International Conference on Data Mining and Information Engineering, Jul 11 2006, pp. 315-328. Prague, Czech Republic: WIT Press.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/33029
dc.description.abstract

Sequential patterns exist in data such as DNA string databases, occurrences of recurrent illness, etc. In this study, we present an algorithm, SEQUEST, to mine frequent subsequences from sequential patterns. The challenges of mining a very large database of sequences is computationally expensive and require large memory space. SEQUEST uses a Direct Memory Access Strips (DMA-Strips) structure to efficiently generate candidate subsequences. DMA-Strips structure provides direct access to each item to be manipulated and thus is optimized for speed and space performance. In addition, the proposed technique uses a hybrid principle of frequency counting by the vertical join approach and candidate generation by structure guided method. The structure guided method is adapted from the TMG approach used for enumerating subtrees in our previous work [8]. Experiments utilizing very large databases of sequences which compare our technique with the existing technique, PLWAP [4], demonstrate the effectiveness of our proposed technique.

dc.publisherWIT Press
dc.relation.urihttp://library.witpress.com/pages/listPapers.asp?q_bid=357
dc.subjectdata mining
dc.subjectsequential strips
dc.subjectsequence
dc.subjectmining frequent subsequences
dc.subjectSEQUEST
dc.subjectDMA-strips
dc.subjectphylogenic tree
dc.titleSEQUEST: Mining frequent subsequences using DMA strips
dc.typeConference Paper
dcterms.source.startPage315
dcterms.source.endPage328
dcterms.source.titleData Mining VII: Data, Text and Web Mining and their Business Applications
dcterms.source.seriesData Mining VII: Data, Text and Web Mining and their Business Applications
dcterms.source.conferenceSeventh International Conference on Data Mining and Information Engineering
dcterms.source.conference-start-dateJul 11 2006
dcterms.source.conferencelocationPrague, Czech Republic
dcterms.source.placeSouthampton, UK
curtin.departmentCentre for Extended Enterprises and Business Intelligence
curtin.identifierEPR-1296
curtin.accessStatusOpen access
curtin.facultyCurtin Business School


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