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Median fuzzy c-means for clustering dissimilarity data

: Geweniger, T.; Zülke, D.; Hammer, B.; Villmann, T.


Neurocomputing 73 (2010), Nr.7-9, S.1109-1116
ISSN: 0925-2312
Fraunhofer FIT ()

Median clustering is a powerful methodology for prototype based clustering of similarity/dissimilarity data. In this contribution we combine the median c-means algorithm with the fuzzy c-means approach, which is only applicable for vectorial (metric) data in its original variant. For the resulted median fuzzy c-means approach we prove convergence and investigate the behavior of the algorithm in several experiments including real world data from psychotherapy research.