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Biased box sampling - a density-biased sampling for clustering

Published:11 March 2007Publication History

ABSTRACT

This paper presents the BBS - Biased Box Sampling algorithm, a technique that combines dimensionality reduction with biased sampling, which aims at keeping the skewed clustering from the original data.

References

  1. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on KDD-96, pages 226--231. AAAI Press, 1996.Google ScholarGoogle Scholar
  2. E. P. M. d. Sousa, C. Traina Jr., A. J. M. Traina, and C. Faloutsos. How to use fractal dimension to find correlations between attributes. In First Workshop on Fractals and Self-similarity in Data Mining: Issues and Approaches (in conjunction with 8th ACM SIGKDD), pages 26--30, Edmonton, Alberta, Canada, 2002. ACM Press.Google ScholarGoogle Scholar

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  1. Biased box sampling - a density-biased sampling for clustering

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    • Published in

      cover image ACM Conferences
      SAC '07: Proceedings of the 2007 ACM symposium on Applied computing
      March 2007
      1688 pages
      ISBN:1595934804
      DOI:10.1145/1244002

      Copyright © 2007 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 11 March 2007

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      Overall Acceptance Rate1,650of6,669submissions,25%

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