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Simulated Annealing C-Means Clustering Algorithm Convergence Proof

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Neural Networks and Soft Computing

Part of the book series: Advances in Soft Computing ((AINSC,volume 19))

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Abstract

The paper presents a simple proof of convergence for the simulated annealing c-means (SACM) algorithm. This proof supports the excellent experimental performances shown by this algorithm that are also due to an accurate modeling of clusters making use of the Mahalanobis distance.

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Author information

Authors and Affiliations

  1. Department of Physics and Biophysics, Medical University of Gdańsk, Dębinki 1, 80-211, Gdańsk, Poland

    Piotr Boguś

  2. INFM National Institute for Physics of Matter, Via Dodecaneso 33, 16146, Genova, Italy

    Anna Maria Massone & Francesco Masulli

  3. Institut of High Energy Physics, Université de Lausanne Bâtiment des Sciences, Physiques CH-1015 Dorigny, Switzerland

    Anna Maria Massone

  4. Department of Computer Sciences, University of Pisa, Corso Italia 40, 56125, Pisa, Italy

    Francesco Masulli

Authors
  1. Piotr Boguś
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  2. Anna Maria Massone
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  3. Francesco Masulli
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Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Technical University Częstochowa, Al. Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

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© 2003 Springer-Verlag Berlin Heidelberg

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Boguś, P., Massone, A.M., Masulli, F. (2003). Simulated Annealing C-Means Clustering Algorithm Convergence Proof. In: Rutkowski, L., Kacprzyk, J. (eds) Neural Networks and Soft Computing. Advances in Soft Computing, vol 19. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1902-1_90

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  • DOI: https://doi.org/10.1007/978-3-7908-1902-1_90

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-0005-0

  • Online ISBN: 978-3-7908-1902-1

  • eBook Packages: Springer Book Archive

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