Probabilistic Proximity Searching Algorithms Based on Compact Partitions

  • Benjamin Bustos
  • Gonzalo Navarro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2476)


The main bottleneck of the research in metric space searching is the so-called curse of dimensionality, which makes the task of searching some metric spaces intrinsically difficult, whatever algorithm is used. A recent trend to break this bottleneck resorts to probabilistic algorithms, where it has been shown that one can find 99% of the elements at a fraction of the cost of the exact algorithm. These algorithms are welcome in most applications because resorting to metric space searching already involves a fuzziness in the retrieval requirements. In this paper we push further in this direction by developing probabilistic algorithms on data structures whose exact versions are the best for high dimensions. As a result, we obtain probabilistic algorithms that are better than the previous ones. We also give new insights on the problem and propose a novel view based on time-bounded searching.


Distance Computation Range Query Query Time Covering Radius Probabilistic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Benjamin Bustos
    • 2
  • Gonzalo Navarro
    • 1
    • 2
  1. 1.1 Center for Web ResearchUniversidad de ChileSantiagoChile
  2. 2.Departamento de Ciencias de la ComputaciónUniversidad de ChileSantiagoChile

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