Approximate reverse k-nearest neighbor queries in general metric spaces

  • Authors:
  • Elke Achtert;Christian Böhm;Peer Kröger;Peter Kunath;Alexey Pryakhin;Matthias Renz

  • Affiliations:
  • Ludwig-Maximilians Universität München, Munich, Germany;Ludwig-Maximilians Universität München, Munich, Germany;Ludwig-Maximilians Universität München, Munich, Germany;Ludwig-Maximilians Universität München, Munich, Germany;Ludwig-Maximilians Universität München, Munich, Germany;Ludwig-Maximilians Universität München, Munich, Germany

  • Venue:
  • CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
  • Year:
  • 2006

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Abstract

In this paper, we propose an approach for efficient approximative RkNN search in arbitrary metric spaces where the value of k is specified at query time. Our method uses an approximation of the nearest-neighbor-distances in order to prune the search space. In several experiments, our solution scales significantly better than existing non-approximative approaches while producing an approximation of the true query result with a high recall.