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Abstract

K-nearest neighbor graph (KNN) is a widely used tool in several pattern recognition applications but it has drawbacks. Firstly, the choice of k can have significant impact on the result because it has to be fixed beforehand, and it does not adapt to the local density of the neighborhood. Secondly, KNN does not guarantee connectivity of the graph. We introduce an alternative data structure called XNN, which has variable number of neighbors and guarantees connectivity. We demonstrate that the graph provides improvement over KNN in several applications including clustering, classification and data analysis.

Keywords

KNN Neighborhood graph Data modeling 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Pasi Fränti
    • 1
    Email author
  • Radu Mariescu-Istodor
    • 1
  • Caiming Zhong
    • 2
  1. 1.University of Eastern FinlandJoensuuFinland
  2. 2.Ningbo UniversityNingboChina

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