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Comparison of Cluster Algorithms for the Analysis of Text Data Using Kolmogorov Complexity

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Advances in Neuro-Information Processing (ICONIP 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5507))

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Abstract

In this paper we present a comparison of multiple cluster algorithms and their suitability for clustering text data. The clustering is based on similarities only, employing the Kolmogorov complexity as a similiarity measure. This motivates the set of considered clustering algorithms which take into account the similarity between objects exclusively. Compared cluster algorithms are Median kMeans, Median Neural Gas, Relational Neural Gas, Spectral Clustering and Affinity Propagation.

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Author information

Authors and Affiliations

  1. Dept. of Medicine, University Leipzig, 04103, Leipzig, Germany

    Tina Geweniger, Frank-Michael Schleif & Thomas Villmann

  2. Clausthal Univ. of Tech., Inst. of CS, 38678, Clausthal, Germany

    Alexander Hasenfuss & Barbara Hammer

Authors
  1. Tina Geweniger
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  2. Frank-Michael Schleif
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  3. Alexander Hasenfuss
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  4. Barbara Hammer
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  5. Thomas Villmann
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Editor information

Editors and Affiliations

  1. Network Design and Research Center, Kyushu Institute of Technology,, 680-4, Kawazu, Iizuka,, 820-8502, Fukuoka, Japan

    Mario Köppen

  2. Knowledge Engineering and Discovery Research Institute (KEDRI), School of Computing and Mathematical Sciences, Auckland University of Technology, 350 Queen Street, 10110, Auckland, New Zealand

    Nikola Kasabov

  3. Department of Electrical and Computer Engineering, Robotics Laboratory, Auckland University of Technology, 38 Princes Street,, 1142, Auckland, New Zealand

    George Coghill

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© 2009 Springer-Verlag Berlin Heidelberg

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Geweniger, T., Schleif, FM., Hasenfuss, A., Hammer, B., Villmann, T. (2009). Comparison of Cluster Algorithms for the Analysis of Text Data Using Kolmogorov Complexity. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_8

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  • DOI: https://doi.org/10.1007/978-3-642-03040-6_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03039-0

  • Online ISBN: 978-3-642-03040-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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