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Comparison of the Performance of Center-Based Clustering Algorithms

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Advances in Knowledge Discovery and Data Mining (PAKDD 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2637))

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Abstract

Center-based clustering algorithms like K-means, and EM are one of the most popular classes of clustering algorithms in use today. The author developed another variation in this family — K-Harmonic Means (KHM). It has been demonstrated using a small number of “benchmark” datasets that KHM is more robust than K-means and EM. In this paper, we compare their performance statistically. We run K-means, K-Harmonic Means and EM on each of 3600 pairs of (dataset, initialization) to compare the statistical average and variation of the performance of these algorithms. The results are that, for low dimensional datasets, KHM performs consistently better than KM, and KM performs consistently better than EM over a large variation of clustered-ness of the datasets and a large variation of initializations. Some of the reasons that contributed to this difference are explained.

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Author information

Authors and Affiliations

  1. Hewlett-Packard Research Laboratories, Palo Alto, 94304, USA

    Bin Zhang

Authors
  1. Bin Zhang
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Editors and Affiliations

  1. Computer Science Department, Korea Advanced Institute of Science and Technology, 373-1 Koo-Sung Dong, Yoo-Sung Ku, Daejeon, 305-701, Korea

    Kyu-Young Whang

  2. Department of Statistics, Seoul National University, Sillimdong Kwanakgu, Seoul, 151-742, Korea

    Jongwoo Jeon

  3. School of Electrical Engineering and Computer Science, Seoul National University, Kwanak P.O. Box 34, Seoul, 151-742, Korea

    Kyuseok Shim

  4. Department of Computer Science and Engineering, University of Minnesota, 200 Union St SE, Minneapolis, MN, 55455, USA

    Jaideep Srivastava

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© 2003 Springer-Verlag Berlin Heidelberg

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Zhang, B. (2003). Comparison of the Performance of Center-Based Clustering Algorithms. In: Whang, KY., Jeon, J., Shim, K., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2003. Lecture Notes in Computer Science(), vol 2637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36175-8_7

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  • DOI: https://doi.org/10.1007/3-540-36175-8_7

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-04760-5

  • Online ISBN: 978-3-540-36175-6

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