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    Incremental clustering of dynamic data streams using connectivity based representative points

    Access Status
    Fulltext not available
    Authors
    Luhr, Sebastian
    Lazarescu, Mihai
    Date
    2009
    Type
    Journal Article
    
    Metadata
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    Citation
    Luhr, S. and Lazarescu, M. 2009. Incremental clustering of dynamic data streams using connectivity based representative points. Data & Knowledge Engineering. 68 (1): pp. 1-27.
    Source Title
    Data & Knowledge Engineering
    DOI
    10.1016/j.datak.2008.08.006
    Additional URLs
    http://portal.acm.org/citation.cfm?id=1464905
    ISSN
    0169023X
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/37647
    Collection
    • Curtin Research Publications
    Abstract

    We present an incremental graph-based clustering algorithm whose design was motivated by a need to extract and retain meaningful information from data streams produced by applications such as large scale surveillance, network packet inspection and financial transaction monitoring. To this end, the method we propose utilises representative points to both incrementally cluster new data and to selectively retain important cluster information within a knowledge repository. The repository can then be subsequently used to assist in the processing of new data, the archival of critical features for off-line analysis, and in the identification of recurrent patterns.

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