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Compact and Efficient Permutations for Proximity Searching

  • Karina Figueroa Mora
  • Rodrigo Paredes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7329)

Abstract

Proximity searching consists in retrieving the most similar objects to a given query. This kind of searching is a basic tool in many fields of artificial intelligence, because it can be used as a search engine to solve problems like \(kN\!N\) searching. A common technique to solve proximity queries is to use an index. In this paper, we show a variant of the permutation based index, which, in his original version, has a great predicting power about which are the objects worth to compare with the query (avoiding the exhaustive comparison). We have noted that when two permutants are close, they can produce small differences in the order in which objects are revised, which could be responsible of finding the true answer or missing it. In this paper we pretend to mitigate this effect. As a matter of fact, our technique allows us both to reduce the index size and to improve the query cost up to 30%.

Keywords

Unitary Cube Distance Computation Range Query Query Time Separation Line 
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 2012

Authors and Affiliations

  • Karina Figueroa Mora
    • 1
  • Rodrigo Paredes
    • 2
  1. 1.Universidad Michoacana de San Nicolás de HidalgoMéxico
  2. 2.Universidad de TalcaChile

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