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Hyper-Spectral Data Clustering Method Based Upon The Sensitive Subspace

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Author Info

  • WENCANG ZHAO

    ()
    (College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266042, China)

  • WEI WANG

    ()
    (College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266042, China)

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    Abstract

    We put forward a quick clustering method for large numbers of data, with high dimensions, which is based on sensitive subspace consisting of the data set's sensitive dimensions. We first estimate the probability density of each dimension by the parzen window algorithm, enhance its optional ability through extracting zeroes and smoothness processing, then through the recognition of the number of the rallying points and the gain of the sensitive dimensions in order to compose the sensitive subspace, and lastly, we perform the Rival Penalized Competitive Learning (RPCL) clustering in the subspace. Moreover, we detected the red tide of hyper-spectral data using this method. Furthermore, the overall improvement in terms of the computational speed is about nine times faster.

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    Bibliographic Info

    Article provided by World Scientific Publishing Co. Pte. Ltd. in its journal New Mathematics and Natural Computation.

    Volume (Year): 03 (2007)
    Issue (Month): 02 ()
    Pages: 271-280

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    Handle: RePEc:wsi:nmncxx:v:03:y:2007:i:02:p:271-280

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    Related research

    Keywords: Sensitive dimension; sensitive subspace; clustering methods; parzen window algorithm; RPCL; hyper-spectral data;

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