Fuzzy Neural Gas for Unsupervised Vector Quantization

In this paper we propose the combination of fuzzy c-means for clustering with neighborhood cooperativeness from the neural gas vector quantizer. The new approach avoids the sensitivity of fuzzy c-means with respect to initialization as it is known from neural gas compared to crisp c-means. Thereby, the neural gas paradigm of neighborhood offers a greater flexibility than those of the self-organizing map, which was combined with fuzzy c-means before. However, a careful reformulation of neighborhood has to be done to keep the validity of the convergence proof of this previous approach. We demonstrate the properties for an artificial as well as for real world data.

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