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Robust Fuzzy Clustering Using Adaptive Fuzzy Meridians

  • Tomasz Przybyła
  • Janusz Jeżewski
  • Janusz Wróbel
  • Krzysztof Horoba
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5990)

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.

Keywords

Fuzzy Cluster Laplace Distribution Medianity Parameter Partition Matrix Fuzzy Cluster Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tomasz Przybyła
    • 1
  • Janusz Jeżewski
    • 2
  • Janusz Wróbel
    • 2
  • Krzysztof Horoba
    • 2
  1. 1.Institute of ElectronicsSilesian University of TechnologyGliwicePoland
  2. 2.Departament of Biomedical InformaticsInstitute of Medical Technology and EquipmentZabrzePoland

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