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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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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DOI: https://doi.org/10.1007/978-3-030-19642-4_26
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