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Impact of the Initialization in Tree-Based Fast Similarity Search Techniques

  • Aureo Serrano
  • Luisa Micó
  • Jose Oncina
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7005)

Abstract

Many fast similarity search techniques relies on the use of pivots (specially selected points in the data set). Using these points, specific structures (indexes) are built speeding up the search when queering. Usually, pivot selection techniques are incremental, being the first one randomly chosen.

This article explores several techniques to choose the first pivot in a tree-based fast similarity search technique. We provide experimental results showing that an adequate choice of this pivot leads to significant reductions in distance computations and time complexity.

Moreover, most pivot tree-based indexes emphasizes in building balanced trees. We provide experimentally and theoretical support that very unbalanced trees can be a better choice than balanced ones.

Keywords

Leaf Node Distance Computation Node Size Query Point Dissimilarity Measure 
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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References

  1. 1.
    Bozkaya, T., Özsoyoglu, Z.M.: Indexing large metric spaces for similarity search queries. ACM Trans. Database Syst. 24(3), 361–404 (1999)CrossRefGoogle Scholar
  2. 2.
    Brin, S.: Near neighbor search in large metric spaces. In: Proceedings of the 21st International Conference on Very Large Data Bases, pp. 574–584 (1995)Google Scholar
  3. 3.
    Chávez, E., Navarro, G., Baeza-Yates, R., Marroquin, J.L.: Searching in metric spaces. ACM Computing Surveys 33(3), 273–321 (2001)CrossRefGoogle Scholar
  4. 4.
    Gómez-Ballester, E., Micó, L., Oncina, J.: Some approaches to improve tree-based nearest neighbour search algorithms. Pattern Recognition 39(2), 171–179 (2006)CrossRefzbMATHGoogle Scholar
  5. 5.
    Faloutsos, C., Lin, K.: Fastmap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets. In: Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, SIGMOD 1995, pp. 163–174. ACM, New York (1995)CrossRefGoogle Scholar
  6. 6.
    Freeman, H.: Boundary encoding and processing. Picture Processing and Psychopictorics, 241–266 (1970)Google Scholar
  7. 7.
    Hjaltason, G.R., Samet, H.: Index-driven similarity search in metric spaces. ACM Trans. Database Syst. 28(4), 517–580 (2003)CrossRefGoogle Scholar
  8. 8.
    Kalantari, I., McDonald, G.: A data structure and an algorithm for the nearest point problem. IEEE Trans. Software Engineering 9, 631–634 (1983)CrossRefzbMATHGoogle Scholar
  9. 9.
    Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. Doklady Akademii Nauk 163(4), 845–848 (1965)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Merkwirth, C., Parlitz, U., Lauterborn, W.: Fast nearest-neighbor searching for nonlinear signal processing. Physical Review 62, 2089–2097 (2000)Google Scholar
  11. 11.
    Micó, L., Oncina, J., Carrasco, R.C.: A fast branch and bound nearest neighbor classifier in metric spaces. Pattern Recognition Letters 17, 731–773 (1996)CrossRefGoogle Scholar
  12. 12.
    Navarro, G., Reyes, N.: Dynamic spatial approximation trees. J. Exp. Algorithmics 12, 1–68 (2008)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Noltemeier, H., Verbarg, K., Zirkelbach, C.: Monotonous bisector* trees – a tool for efficient partitioning of complex scenes of geometric objects. In: Monien, B., Ottmann, T. (eds.) Data Structures and Efficient Algorithms. LNCS, vol. 594, pp. 186–203. Springer, Heidelberg (1992)CrossRefGoogle Scholar
  14. 14.
    Shapiro, M.: The choice of reference points in best-match file searching. Commun. ACM 20, 339–343 (1977)CrossRefGoogle Scholar
  15. 15.
    Uhlmann, J.K.: Satisfying general proximity/similarity queries with metric trees. Inf. Process. Lett. 40(4), 175–179 (1991)CrossRefzbMATHGoogle Scholar
  16. 16.
    Wagner, R.A., Fischer, M.J.: The string-to-string correction problem. Journal of the ACM 21(1), 168–173 (1974)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Yianilos, P.N.: Data structures and algorithms for nearest neighbor search in general metric spaces. In: Proceedings of the ACM-SIAM Symposium on Discrete Algorithms, pp. 311–321 (1993)Google Scholar
  18. 18.
    Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach. Springer, Heidelberg (2006)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Aureo Serrano
    • 1
  • Luisa Micó
    • 1
  • Jose Oncina
    • 1
  1. 1.Departamento de Lenguajes y Sistemas InformáticosUniversidad de AlicanteAlicanteSpain

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