Multiple Prototype Model for Fuzzy Clustering

  • Susana Nascimento
  • Boris Mirkin
  • Fernando Moura-Pires
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1642)


In partitional fuzzy clustering, each cluster is characterized by two items: its centroid and its membership function, that are usually interconnected through distances between centroids and entities (as in fuzzy c-means).

We propose a different framework for partitional fuzzy clustering which suggests a model of how the data are generated from a cluster structure to be identified. In the model, we assume that the membership of each entity to a cluster expresses a part of the cluster prototype reflected in the entity. Due to many restrictions imposed, the model as is leads to removing of unneeded cluster prototypes and, thus, can serve as an index of the number of clusters present in data.

A comparative experimental study of the method fitting the model, its relaxed version and the fuzzy c-means algorithm has been undertaken. In general, the study suggests that our methods can be considered a model-based parallel to the fuzzy c-means approach. Moreover, our generic version can be viewed as a device for revealing “the natural cluster structure” hidden in data.


Cluster Structure Fuzzy Cluster Gradient Projection Method Major Iteration Comparative Experimental Study 
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 1999

Authors and Affiliations

  • Susana Nascimento
    • 1
  • Boris Mirkin
    • 2
  • Fernando Moura-Pires
    • 1
  1. 1.Departamento de InformáaticaFaculdade Ciências e Tecnologia-Universidade Nova de LisboaPortugal
  2. 2.DIMACSRutgers UniversityUSA

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