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Efficient nearest-neighbour searches using weighted euclidean metrics

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Advances in Databases (BNCOD 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1405))

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Abstract

Building an index tree is a common approach to speed up the k nearest neighbour search in large databases of many-dimensional records. Many applications require varying distance metrics by putting a weight on different dimensions. The main problem with k nearest neighbour searches using weighted euclidean metrics in a high dimensional space is whether the searches can be done efficiently. We present a solution to this problem which uses the bounding rectangle of the nearest-neighbour disk instead of using the disk directly. The algorithm is able to perform nearest-neighbour searches using distance metrics different from the metric used to build the search tree without having to rebuild the tree. It is efficient for weighted euclidean distance and extensible to higher dimensions.

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Authors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, 2052, Sydney, Australia

    Ruth Kurniawati, Jesse S. Jin & John A. Shepherd

Authors
  1. Ruth Kurniawati
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  2. Jesse S. Jin
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  3. John A. Shepherd
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Corresponding author

Correspondence to Ruth Kurniawati .

Editor information

Suzanne M. Embury Nicholas J. Fiddian W. Alex Gray Andrew C. Jones

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© 1998 Springer-Verlag Berlin Heidelberg

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Kurniawati, R., Jin, J.S., Shepherd, J.A. (1998). Efficient nearest-neighbour searches using weighted euclidean metrics. In: Embury, S.M., Fiddian, N.J., Gray, W.A., Jones, A.C. (eds) Advances in Databases. BNCOD 1998. Lecture Notes in Computer Science, vol 1405. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0053472

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  • DOI: https://doi.org/10.1007/BFb0053472

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64659-4

  • Online ISBN: 978-3-540-69112-9

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