Advertisement

Hierarchical Clustering-Merging for Multidimensional Index Structures

  • Zhan Chen
  • Jing Ding
  • Mu Zhang
  • Wallapak Tavanapong
  • Johnny S. Wong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2728)

Abstract

The R-tree family index structures are among the most common index structures used in multidimensional databases. To improve the search performance it is very important to reduce the overlap between bounding regions in the R-tree. However the arbitrary insertion order in the tree construction procedure might result in tree structures inefficient in the search operations. In this paper we propose a new technique called Hierarchical Clustering-Merging (HCM) to improve the tree construction procedure of the R-tree family index structures. With this technique we can take advantage of the data distribution information in the data set to achieve an optimized tree structure and improve the search performance.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    O. Guttman, “R-tree: A Dynamic Index Structure for Spatial Searching”, in Proc. ACM SIGMOD, pp.47–57, 1984Google Scholar
  2. [2]
    N. Katayama and S. Satoh, “The SR-tree: An Index Structure for High-Dimensional Nearest Neighbor Queries”, In Proc. of the ACM SIGMOD, pp. 369–380, 1997Google Scholar
  3. [3]
    N. Beckmann, H.P. Kriegel, R. Schneider, B. Seeger, “The R*-tree: An Efficient and Robust Access Method for Points and Rectangles”, In Proc. of ACM SIGMOD International Conference on Management of Data, pp. 322–331, May 1990Google Scholar
  4. [4]
    T. Sellis, N. Roussopoulos, and C. Faloutsos, “The R+-tree: A dynamic index for multi-dimensional objects,” in Proc. of 13th Conf. Very Large Databases, Brighton, U.K., pp. 507–518, Sept. 1987Google Scholar
  5. [5]
    I. Kamel, C. Faloutsos, “On Packing R-trees”, in Proc. of the 2nd International Conference on Information and Knowledge Management, pp. 490–499, Arlington, VA, November 1993Google Scholar
  6. [6]
    Roussopoulos, D. Leifker, “Direct Spatial Search on Pictorial Databases Using Packed R-trees”, in Proc. of ACM SIGMOD International Conference on Management of Data, pp. 17–31, 1985Google Scholar
  7. [7]
    Scott T. Leutenegger et al, “STR: A Simple and Efficient Algorithm for R-tree Packing”, in Proc. of the 13rd IEEE International Conference on Data Engineering, pp. 497–506, Birmingham U.K., 1997Google Scholar
  8. [8]
    Ng and J. Han, “Efficient and Effective Clustering Method for Spatial Data Mining”, VLDB’94, pp. 144–155, Santiago, Chile, Sept. 1994Google Scholar
  9. [9]
    A.K. Jian, M.N. Murty, and P. J. Flynn, “Data clustering: A review”, ACM Computing Sur-veys, vol. 31, no. 3, September 1999Google Scholar
  10. [10]
    G. Lu, “Techniques and data structures for efficient multimedia retrieval based on similarity”, pp. 372–384, IEEE Transactions on Multimedia, Vol. 4, No. 3, Sept. 2002CrossRefGoogle Scholar
  11. [11]
    Open Source Clustering Software: http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster/index.htmlGoogle Scholar
  12. [12]
    Source code of multi-dimensional indexing techniques: http://dias.cti.gr/~ytheod/research/indexing/Google Scholar
  13. [13]
    The SR-tree: http://research.nii.ac.jp/~katayama/homepage/research/srtree/English.htmlGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Zhan Chen
    • 1
  • Jing Ding
    • 1
  • Mu Zhang
    • 1
  • Wallapak Tavanapong
    • 1
  • Johnny S. Wong
    • 1
  1. 1.Department of Computer ScienceIowa State UniversityAmesUSA

Personalised recommendations