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    SEQUEST: Mining frequent subsequences using DMA strips

    20083_downloaded_stream_71.pdf (452.0Kb)
    Access Status
    Open access
    Authors
    Tan, H.
    Dillon, Tharam S.
    Hadzic, Fedja
    Chang, Elizabeth
    Date
    2006
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Tan, 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.
    Source Title
    Data Mining VII: Data, Text and Web Mining and their Business Applications
    Source Conference
    Seventh International Conference on Data Mining and Information Engineering
    Additional URLs
    http://library.witpress.com/pages/listPapers.asp?q_bid=357
    Faculty
    Curtin Business School
    School
    Centre for Extended Enterprises and Business Intelligence
    URI
    http://hdl.handle.net/20.500.11937/33029
    Collection
    • Curtin Research Publications
    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.

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