Testing Some Improvements of the Fukunaga and Narendra’s Fast Nearest Neighbour Search Algorithm in a Spelling Task

  • Eva Gómez-Ballester
  • Luisa Micó
  • Jose Oncina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3523)

Abstract

Nearest neighbour search is one of the most simple and used technique in Pattern Recognition.

One of the most known fast nearest neighbour algorithms was proposed by Fukunaga and Narendra. The algorithm builds a tree in preprocess time that is traversed on search time using some elimination rules to avoid its full exploration.

This paper tests two new types of improvements in a real data environment, a spelling task. The first improvement is a new (and faster to build) type of tree, and the second is the introduction of two new elimination rules.

Both techniques, even taken independently, reduce significantly both: the number of distance computations and the search time expended to find the nearest neighbour.

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References

  1. 1.
    Dasarathy, B.V.: Nearest Neighbour(NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, Los Alamitos (1991)Google Scholar
  2. 2.
    Chen, Y.S., Hung, Y.P., Fuh, C.S.: Fast Algorithm for Nearest Neighbour Search Based on a Lower Bound Tree. In: Proceedings of the 8th International Conference on Computer Vision, Vancouver, Canada, vol. 1, pp. 446–453 (2001)Google Scholar
  3. 3.
    Duda, R., Hart, P.: Pattern Classification and Scene Analysis. Wiley, Chichester (1973)MATHGoogle Scholar
  4. 4.
    Fukunaga, K., Narendra, M.: A branch and bound algorithm for computing k– nearest neighbours. IEEE Trans. Computing 24, 750–753 (1975)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Kalantari, I., McDonald, G.: A data structure and an algorithm for the nearest point problem. IEEE Trans. Software Engineering 9, 631–634 (1983)CrossRefGoogle Scholar
  6. 6.
    Micó, L., Oncina, J., Carrasco, R.C.: A fast branch and bound nearest neighbour classifier in metric spaces. Pattern Recognition Letters 17, 731–739 (1996)CrossRefGoogle Scholar
  7. 7.
    Alinat, P.: Periodic progress report 4, ROARS project ESPRIT II - Number 5516. Thomson Technical Report TS ASM 93/S/EGS/NC/079 (1993)Google Scholar
  8. 8.
    Omachi, S., Aso, H.: A fast algorithm for a k-NN classifier based on branch and bound method and computational quantity estimation. Systems and Computers in Japan 31(6), 1–9 (2000)CrossRefGoogle Scholar
  9. 9.
    Geofrey, I.: Webb: OPUS: An Efficient Admissible Algorithm for Unordered Search. Journal of Artificial Intelligence Research 3, 431–465 (1995)Google Scholar
  10. 10.
    Kamgar-Parsi, B., Kanal, L.: An improved branch and bound algorithm for computing k-nearest neighbors. Pattern Recognition Letters 3, 7–12 (1985)CrossRefGoogle Scholar
  11. 11.
    Gómez-Ballester, E., Micó, L., Oncina, J.: Some improvements in tree based nearest neighbour algorithms. In: Sanfeliu, A., Ruiz-Shulcloper, J. (eds.) CIARP 2003. LNCS, vol. 2905, pp. 456–463. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Eva Gómez-Ballester
    • 1
  • Luisa Micó
    • 1
  • Jose Oncina
    • 1
  1. 1.Dept. Lenguajes y Sistemas InformáticosUniversidad de AlicanteAlicanteSpain

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