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    Stream Quantiles via Maximal Entropy Histograms

    Access Status
    Fulltext not available
    Authors
    Arandjelovic, O.
    Pham, DucSon
    Venkatesh, S.
    Date
    2014
    Type
    Conference Paper
    
    Metadata
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    Citation
    Arandjelovic, O. and Pham, D. and Venkatesh, S. 2014. Stream Quantiles via Maximal Entropy Histograms, in Loo, C.K., Keem Siah, Y., Wong, K.K.W., Beng Jin, A.T. & Huang, K. (ed), The 21st International Conference on Neural Information Processing (ICONIP 2014), Nov 3 2014, pp. 327-334. Kuching, Malaysia: Springer.
    Source Title
    Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8835.
    Source Conference
    The 21st International Conference on Neural Information Processing (ICONIP 2014)
    DOI
    10.1007/978-3-319-12640-1_40
    ISBN
    978-3-319-12639-5
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/49483
    Collection
    • Curtin Research Publications
    Abstract

    We address the problem of estimating the running quantile of a data stream when the memory for storing observations is limited. We (i) highlight the limitations of approaches previously described in the literature which make them unsuitable for non-stationary streams, (ii) describe a novel principle for the utilization of the available storage space, and (iii) introduce two novel algorithms which exploit the proposed principle. Experiments on three large real-world data sets demonstrate that the proposed methods vastly outperform theexisting alternatives.

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