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International Conference on Artificial Neural Networks

ICANN 2006: Artificial Neural Networks – ICANN 2006 pp 437–443Cite as

Neural Network Clustering Based on Distances Between Objects

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4132))

Abstract

We present an algorithm of clustering of many-dimensional objects, where only the distances between objects are used. Centers of classes are found with the aid of neuron-like procedure with lateral inhibition. The result of clustering does not depend on starting conditions. Our algorithm makes it possible to give an idea about classes that really exist in the empirical data. The results of computer simulations are presented.

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References

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Author information

Authors and Affiliations

  1. Institute of Optical-Neural Technologies Russian Academy of Sciences, Moscow

    Leonid B. Litinskii & Dmitry E. Romanov

Authors
  1. Leonid B. Litinskii
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  2. Dmitry E. Romanov
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Editor information

Editors and Affiliations

  1. School of Electrical and Computer Engineering, Image, Video and Multimedia Systems Laboratory, National Technical University of Athens, 157 80, Zographou, GR, Greece

    Stefanos Kollias

  2. Department of Electrical and Computer Engineering, National Technical University of Athens, 15780, Zographou, Greece

    Andreas Stafylopatis

  3. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  4. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015, HUT, Finland

    Erkki Oja

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© 2006 Springer-Verlag Berlin Heidelberg

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Cite this paper

Litinskii, L.B., Romanov, D.E. (2006). Neural Network Clustering Based on Distances Between Objects. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_45

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  • DOI: https://doi.org/10.1007/11840930_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-38871-5

  • Online ISBN: 978-3-540-38873-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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