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  • A New Indexing Method for High Dimensional Dataset

    Author(s)
    An, Jiyuan
    Chen, Yi-Ping Phoebe
    Xu, Qinying
    Zhou, Xiaofang
    Griffith University Author(s)
    An, Jay
    Year published
    2005
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    Abstract
    Indexing high dimensional datasets has attracted extensive attention from many researchers in the last decade. Since R-tree type of index structures are known as suffering curse of dimensionality problems, Pyramid-tree type of index structures, which are based on the B-tree, have been proposed to break the curse of dimensionality. However, for high dimensional data, the number of pyramids is often insufficient to discriminate data points when the number of dimensions is high. Its effectiveness degrades dramatically with the increase of dimensionality. In this paper, we focus on one particular issue of curse of dimensionality; ...
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    Indexing high dimensional datasets has attracted extensive attention from many researchers in the last decade. Since R-tree type of index structures are known as suffering curse of dimensionality problems, Pyramid-tree type of index structures, which are based on the B-tree, have been proposed to break the curse of dimensionality. However, for high dimensional data, the number of pyramids is often insufficient to discriminate data points when the number of dimensions is high. Its effectiveness degrades dramatically with the increase of dimensionality. In this paper, we focus on one particular issue of curse of dimensionality; that is, the surface of a hypercube in a high dimensional space approaches 100% of the total hypercube volume when the number of dimensions approaches infinite. We propose a new indexing method based on the surface of dimensionality. We prove that the Pyramid tree technology is a special case of our method. The results of our experiments demonstrate clear priority of our novel method.
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    Journal Title
    Lecture Notes in Computer Science
    Volume
    3453
    DOI
    https://doi.org/10.1007/11408079_35
    Publication URI
    http://hdl.handle.net/10072/25632
    Collection
    • Journal articles

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