Robust fuzzy clustering using adaptive fuzzy meridians

Authors:
Tomasz Przybyła;Janusz Jeżewski;Janusz Wróbel;Krzysztof Horoba
Affiliations:
Silesian University of Technology, Institute of Electronics, Gliwice, Poland;Institute of Medical Technology and Equipment, Departament of Biomedical Informatics, Zabrze, Poland;Institute of Medical Technology and Equipment, Departament of Biomedical Informatics, Zabrze, Poland;Institute of Medical Technology and Equipment, Departament of Biomedical Informatics, Zabrze, Poland
Venue:
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
Year:
2010

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Abstract

The fuzzy clustering methods are useful in the data mining applications. This paper describes a new fuzzy clustering method in which each cluster prototype is calculated as a fuzzy meridian. The meridian is the maximum likelihood estimator of the location for the meridian distribution. The value of the meridian depends on the data samples and also depends on the medianity parameter. The sample meridian is extended to fuzzy sets to define a fuzzy meridian. For the estimation of medianity parameter value, the classical Parzen window method by real non-negative weights has been generalized. An example illustrating the robustness of the proposed method was given.