Counting distance permutations

  • Authors:
  • Matthew Skala

  • Affiliations:
  • David R. Cheriton School of Computer Science University of Waterloo, Waterloo, Ontario, Canada N2L 3G1

  • Venue:
  • Journal of Discrete Algorithms
  • Year:
  • 2009

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Abstract

Distance permutation indexes support fast proximity searching in high-dimensional metric spaces. Given some fixed reference sites, for each point in a database the index stores a permutation naming the closest site, the second-closest, and so on. We examine how many distinct permutations can occur as a function of the number of sites and the size of the space. We give theoretical results for tree metrics and vector spaces with L"1, L"2, and L"~ metrics, improving on the previous best known storage space in the vector case. We also give experimental results and commentary on the number of distance permutations that actually occur in a variety of vector, string, and document databases.