Principal Directions-Based Pivot Placement

  • Fabrizio Angiulli
  • Fabio Fassetti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8199)

Abstract

Determining a good sets of pivots is a challenging task for metric space indexing. Several techniques to select pivots from the data to be indexed have been introduced in the literature. In this paper, we propose a pivot placement strategy which exploits the natural data orientation in order to select space points which achieve a good alignment with the whole data to be indexed. Comparison with existing methods substantiates the effectiveness of the approach.

Keywords

Range Query Query Object Pattern Recognition Letter Pivot Selection Dataset Object 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Angiulli, F., Fassetti, F.: Indexing uncertain data in general metric spaces. IEEE Trans. Knowl. Data Eng. 24(9), 1640–1657 (2012)CrossRefGoogle Scholar
  2. 2.
    Ares, L., Brisaboa, N., Esteller, M., Pedreira, O., Places, A.: Optimal pivots to minimize the index size for metric access methods. In: International Workshop on Similarity Search and Applications (SISAP), pp. 74–80 (2009)Google Scholar
  3. 3.
    Bache, K., Lichman, M.: UCI machine learning repository (2013)Google Scholar
  4. 4.
    Bustos, B., Navarro, G., Chávez, E.: Pivot selection techniques for proximity searching in metric spaces. Pattern Recognition Letters 24(14), 2357–2366 (2003)MATHCrossRefGoogle Scholar
  5. 5.
    Bustos, B., Pedreira, O., Brisaboa, N.R.: A dynamic pivot selection technique for similarity search. In: ICDE Workshops, pp. 394–401 (2008)Google Scholar
  6. 6.
    Chávez, E., Marroquín, J.L., Baeza-Yates, R.A.: Spaghettis: An array based algorithm for similarity queries in metric spaces. In: Symp. on String Processing and Information Retrieval (SPIRE), pp. 38–46 (1999)Google Scholar
  7. 7.
    Chávez, E., Navarro, G., Baeza-Yates, R., Marroquín, J.: Searching in metric spaces. ACM Computing Surveys 33(3), 273–321 (2001)CrossRefGoogle Scholar
  8. 8.
    Figueroa, K., Navarro, G., Chávez, E.: Metric spaces library (2007), http://www.sisap.org/Metric_Space_Library.html
  9. 9.
    Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer (October 2002)Google Scholar
  10. 10.
    Micó, L., Oncina, J., Vidal, E.: A new version of the nearest-neighbour approximating and eliminating search algorithm (aesa) with linear preprocessing time and memory requirements. Pattern Recognition Letters 15(1), 9–17 (1994)CrossRefGoogle Scholar
  11. 11.
    Pedreira, O., Brisaboa, N.R.: Spatial selection of sparse pivots for similarity search in metric spaces. In: van Leeuwen, J., Italiano, G.F., van der Hoek, W., Meinel, C., Sack, H., Plášil, F. (eds.) SOFSEM 2007. LNCS, vol. 4362, pp. 434–445. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Samet, H.: Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann Publishers Inc. (2005)Google Scholar
  13. 13.
    Yianilos, P.N.: Data structures and algorithms for nearest neighbor search in general metric spaces. In: ACM-SIAM Symp. on Discrete Algorithms (SODA), pp. 311–321 (1993)Google Scholar
  14. 14.
    Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach. Advances in Database Systems, vol. 32. Springer (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fabrizio Angiulli
    • 1
  • Fabio Fassetti
    • 1
  1. 1.DIMES DepartmentUniversity of CalabriaRendeItaly

Personalised recommendations