Optimization of Parametrized Divergences in Fuzzy c-Means

We propose the utilization of divergences as dissimilarity mea- sure in the Fuzzy c-Means algorithm for the clustering of functional data. Further we adapt the relevance parameter to improve the data representa- tion and therefore obtain more accurate clusterings in terms of separation and compactness. We show for two example applications that this method leads to improved performance.

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