Skip to main content
Springer Nature Link
Log in

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

  • Conference paper
Advances in Neuro-Information Processing (ICONIP 2008)

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

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Hammer, B., Hasenfuss, A.: Relational neural gas. Künstliche Intelligenz, 190–204 (2007)

    Google Scholar 

  2. Cilibrasi, R., Vitányi, P.: Clustering by compression. IEEE Transactions on Information Theory 51(4), 1523–1545 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  3. Cottrell, M., Hammer, B., HasenfuSS, A., Villmann, T.: Batch and median neural gas. Neural Networks 19, 762–771 (2006)

    Article  MATH  Google Scholar 

  4. Dou, W., Ren, Y., Wu, Q., Ruan, S., Chen, Y., Constans, D.B.A.-M.: Fuzzy kappa for the agreement measure of fuzzy classifications. Neurocomputing 70, 726–734 (2007)

    Article  Google Scholar 

  5. Fleiss, J.: Statistical Methods for Rates and Proportions, 2nd edn. Wiley, New York (1981)

    MATH  Google Scholar 

  6. Frey, B., Dueck, D.: Clustering by message passing between data points. Science 315, 972–976 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  7. Kohonen, T.: Self-Organizing Maps. Springer Series in Information Sciences, vol. 30. Springer, Heidelberg (1995) (2nd extended edn. 1997)

    Google Scholar 

  8. Luxburg, U.V.: A tutorial on spectral clustering. Statistics and Computing 17(4), 395–416 (2007)

    Article  MathSciNet  Google Scholar 

  9. Martinetz, T.M., Berkovich, S.G., Schulten, K.J.: ‘Neural-gas’ network for vector quantization and its application to time-series prediction. IEEE Trans. on Neural Networks 4(4), 558–569 (1993)

    Article  Google Scholar 

  10. Villmann, T., Liebers, C., Bergmann, B., Gumz, A., Geyer, M.: Investigation of psycho-physiological interactions between patient and therapist during a psychodynamic therapy and their relation to speech in terms of entropy analysis using a neural network approach. New Ideas in Psychology 26, 309–325 (2008)

    Article  Google Scholar 

Download references

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frank-Michael Schleif
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexander Hasenfuss
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Barbara Hammer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Thomas Villmann
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

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

Download citation

  • 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)

Publish with us

Policies and ethics