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Trail-and-Error Approach for Determining the Number of Clusters

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Advances in Machine Learning and Cybernetics

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3930))

Abstract

Automatically determining the number of clusters is an important issue in cluster analysis. In this paper, we explore “trial-and-error” approach to determining the number of clusters in a given data set. The fuzzy clustering algorithm, FCM, is selected as the basic “trial” algorithm and cluster validity optimization responses to the “error” procedure. To improve the computation speed, we propose two strategies, eliminating and splitting, which allow the FCM-based algorithms more efficient. To improve existing validity measures, we make use of a new validity function that fits particularly data sets containing overlapping clusters. Experimental results are given to illustrate the performance of the new algorithms.

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Authors and Affiliations

  1. College of Mathematics and Computer Science, University of Hebei, Baoding, Hebei, 071002, China

    Haojun Sun & Mei Sun

Authors
  1. Haojun Sun
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  2. Mei Sun
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Editor information

Editors and Affiliations

  1. Department of Computing, Hong Kong Polytechnic University, P.O. Box, Hong Kong, China

    Daniel S. Yeung

  2. School of Creative Media, City University of Hong Kong,, China

    Zhi-Qiang Liu

  3. Department of Mathematics and Computer Science, Hebei University, 071002, Baoding, Hebei, P.R. China

    Xi-Zhao Wang

  4. School of Electrical and Information Engineering, University of Sydney, 2006, NSW, Australia

    Hong Yan

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© 2006 Springer-Verlag Berlin Heidelberg

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Sun, H., Sun, M. (2006). Trail-and-Error Approach for Determining the Number of Clusters. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_24

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  • DOI: https://doi.org/10.1007/11739685_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33584-9

  • Online ISBN: 978-3-540-33585-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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