An Effective Permutant Selection Heuristic for Proximity Searching in Metric Spaces

  • Karina Figueroa
  • Rodrigo Paredes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8495)


The permutation based index has shown to be very effective in medium and high dimensional metric spaces, even in difficult problems such as solving reverse k-nearest neighbor queries. Nevertheless, currently there is no study about which are the desirable features one can ask to a permutant set, or how to select good permutants. Similar to the case of pivots, our experimental results show that, compared with a randomly chosen set, a good permutant set yields to fast query response or to reduce the amount of space used by the index. In this paper, we start by characterizing permutants and studying their predictive power; then we propose an effective heuristic to select a good set of permutant candidates. We also show empirical evidence that supports our technique.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Karina Figueroa
    • 1
  • Rodrigo Paredes
    • 2
  1. 1.Facultad de Ciencias Físico-MatemáticasUniversidad MichoacanaMéxico
  2. 2.Departamento de Ciencias de la ComputaciónUniversidad de TalcaChile

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