NNH: Improving Performance of Nearest-Neighbor Searches Using Histograms

  • Liang Jin
  • Nick Koudas
  • Chen Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2992)


Efficient search for nearest neighbors (NN) is a fundamental problem arising in a large variety of applications of vast practical interest. In this paper we propose a novel technique, called NNH (“Nearest Neighbor Histograms”), which uses specific histogram structures to improve the performance of NN search algorithms. A primary feature of our proposal is that such histogram structures can co-exist in conjunction with a plethora of NN search algorithms without the need to substantially modify them. The main idea behind our proposal is to choose a small number of pivot objects in the space, and pre-calculate the distances to their nearest neighbors. We provide a complete specification of such histogram structures and show how to use the information they provide towards more effective searching. In particular, we show how to construct them, how to decide the number of pivots, how to choose pivot objects, how to incrementally maintain them under dynamic updates, and how to utilize them in conjunction with a variety of NN search algorithms to improve the performance of NN searches. Our intensive experiments show that nearest neighbor histograms can be efficiently constructed and maintained, and when used in conjunction with a variety of algorithms for NN search, they can improve the performance dramatically.


Near Neighbor Priority Queue Query Point Queue Size Pivot Point 
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 2004

Authors and Affiliations

  • Liang Jin
    • 1
  • Nick Koudas
    • 2
  • Chen Li
    • 1
  1. 1.Information and Computer ScienceUniversity of CaliforniaIrvineUSA
  2. 2.AT&T Labs ResearchFlorham ParkUSA

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