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COE: Clustering with Obstacles Entities A Preliminary Study

  • Anthony K. H. Tung
  • Jean Hou
  • Jiawei Han
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1805)

Abstract

Clustering analysis has been a very active area of research in the data mining community. However, most algorithms have ignored the fact that physical obstacles exist in the real world and could thus affect the result of clustering dramatically. In this paper, we will look at the problem of clustering in the presence of obstacles. We called this problem the COE (Clustering with Obstacles Entities) problem and provide an outline of an algorithm called COE-CLARANS to solve it.

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References

  1. [BFR98]
    P. Bradley, U. Fayyad, and C. Reina. Scaling clustering algorithms to large databases. In Proc. 4th Int. Conf. Knowledge Discovery and Data Mining (KDD’98), pages 9–15, New York, NY, August 1998.Google Scholar
  2. [HK00]
    J. Han and M. Kamber. Data Mining: Concepts and Techniques. (to be published by) Morgan Kaufmann, 2000.Google Scholar
  3. [KHK99]
    G. Karypis, E.-H. Han, and V. Kumar. CHAMELEON: A hierarchical clustering algorithm using dynamic modeling. COMPUTER, 32:68–75, 1999.CrossRefGoogle Scholar
  4. [NH94]
    R. Ng and J. Han. Efficient and effective clustering method for spatial data mining. In Proc. 1994 Int. Conf. Very Large Data Bases, pages 144–155, Santiago, Chile, September 1994.Google Scholar
  5. [ZRL96]
    T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH: an efficient data clustering method for very large databases. In Proc. 1996 ACM-SIGMOD Int. Conf. Management of Data, pages 103–114, Montreal, Canada, June 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Anthony K. H. Tung
    • 1
  • Jean Hou
    • 1
  • Jiawei Han
    • 1
  1. 1.School of Computing ScienceSimon Eraser UniversityCanada

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