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International Workshop on Self-Organizing Maps

WSOM 2019: Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization pp 261–270Cite as

Variants of Fuzzy Neural Gas

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 976))

Abstract

Neural Gas is a prototype based clustering technique taking the ranking of the prototypes regarding their distance to the data samples into account. Previously, we proposed a fuzzy version of this approach, yet restricted our method to probabilistic cluster assignments. In this paper we extend this method by combining possibilistic and probabilistic assignments. Further we provide modifications to handle non-vectorial data.

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Author information

Authors and Affiliations

  1. Westsächsische Hochschule Zwickau – University of Applied Sciences, Zwickau, Germany

    Tina Geweniger

  2. Hochschule Mittweida – University of Applied Sciences, Mittweida, Germany

    Tina Geweniger & Thomas Villmann

Authors
  1. Tina Geweniger
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  2. Thomas Villmann
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Corresponding author

Correspondence to Thomas Villmann .

Editor information

Editors and Affiliations

  1. Department of Computer Science, UPC BarcelonaTech, Barcelona, Spain

    Alfredo Vellido

  2. Knowledge Engineering and Machine Learning Group (KEMLG) at Intelligent Data Science and Artificial Intelligence Research Center, UPC BarcelonaTech, Barcelona, Spain

    Karina Gibert

  3. Department of Automatic Control, UPC BarcelonaTech, Barcelona, Spain

    Cecilio Angulo

  4. Departament d'Enginyeria Electrònica, Universitat de València, Burjassot, Valencia, Spain

    José David Martín Guerrero

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Geweniger, T., Villmann, T. (2020). Variants of Fuzzy Neural Gas. In: Vellido, A., Gibert, K., Angulo, C., Martín Guerrero, J. (eds) Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM 2019. Advances in Intelligent Systems and Computing, vol 976. Springer, Cham. https://doi.org/10.1007/978-3-030-19642-4_26

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