Median fuzzy c-means for clustering dissimilarity data

Authors:
Tina Geweniger;Dietlind Zülke;Barabara Hammer;Thomas Villmann
Affiliations:
Medical Department, Universität Leipzig, Germany;Fraunhofer Institute for Applied Information Technology - Life Science Informatics, Germany;Institute of Computer Science, Clausthal University of Technology, Germany;Department of Mathematics, University of Applied Sciences Mittweida, Germany
Venue:
Neurocomputing
Year:
2010

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Abstract

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.