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Cluster Validity Measures Based on the Minimum Description Length Principle

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Knowledge-Based and Intelligent Information and Engineering Systems (KES 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6881))

Abstract

Determining the number of clusters is a crucial problem in cluster analysis. Cluster validity measures are one way to try to find the optimum number of clusters, especially for prototype-based clustering. However, no validity measure turns out to work well in all cases. In this paper, we propose an approach to determine the number of cluster based on the minimum description length principle which does not need high computational costs and is also applicable in the context of fuzzy clustering.

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Author information

Authors and Affiliations

  1. Department of Software Engineering, Faculty of Mathematics and Informatics, Sofia University, 125 Tzarigradsko shose Blvd., 1113, Sofia, Bulgaria

    Olga Georgieva

  2. Department of Computer Science, Ostfalia University of Applied Sciences, Salzdahlumer Str. 46/48, D-38302, Wolfenbuettel, Germany

    Katharina Tschumitschew & Frank Klawonn

  3. Bioinformatics and Statistics, Helmholtz Centre for Infection Research, Inhoffenstr. 7, D-38124, Braunschweig, Germany

    Frank Klawonn

Authors
  1. Olga Georgieva
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  2. Katharina Tschumitschew
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  3. Frank Klawonn
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Editor information

Editors and Affiliations

  1. Institute of Integrated Sensor Systems, University of Kaiserslautern, Erwin-Schroedinger-str. 12, 67663, Kaiserslautern, Germany

    Andreas König

  2. Knowledge-Based Systems Group, Department of omputer Science, University of Kaiserslautern, P.O. Box 3049, 67653, Kaiserslautern, Germany

    Andreas Dengel

  3. School of Business, University of Applied Sciences Northwestern Switzerland, Riggenbachstr. 16, 4600, Olten, Switzerland

    Knut Hinkelmann

  4. Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, 599-8531, Sakai, Osaka, Japan

    Koichi Kise

  5. KES International, P.O. Box 2115, BN43 9AF, Shoreham-by-sea, UK

    Robert J. Howlett

  6. University of South Australia, Mawson Lakes, 5095, Adelaide, SA, Australia

    Lakhmi C. Jain

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© 2011 Springer-Verlag Berlin Heidelberg

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Georgieva, O., Tschumitschew, K., Klawonn, F. (2011). Cluster Validity Measures Based on the Minimum Description Length Principle. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2011. Lecture Notes in Computer Science(), vol 6881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23851-2_9

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  • DOI: https://doi.org/10.1007/978-3-642-23851-2_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23850-5

  • Online ISBN: 978-3-642-23851-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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