GeCiM: A Novel Generalized Approach to C-Means Clustering

  • László Szilágyi
  • David Iclănzan
  • Sándor M. Szilágyi
  • Dan Dumitrescu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5197)


All three conventional c-means clustering algorithms have their advantages and disadvantages. This paper presents a novel generalized approach to c-means clustering: the objective function is considered to be a mixture of the FCM, PCM, and HCM objective functions. The optimal solution is obtained via evolutionary computation. Our main goal is to reveal the properties of such mixtures and to formulate some rules that yield accurate partitions.


fuzzy c-means clustering possibilistic c-means clustering hard c-means clustering evolutionary computation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • László Szilágyi
    • 1
    • 2
  • David Iclănzan
    • 1
    • 3
  • Sándor M. Szilágyi
    • 1
  • Dan Dumitrescu
    • 3
  1. 1.SapientiaHungarian Science University of Transylvania, Faculty of Technical and Human ScienceTârgu-MureşRomania
  2. 2.Department of Control Engineering and Information TechnologyBudapest University of Technology and EconomicsBudapestHungary
  3. 3.Faculty of Mathematics and Computer ScienceBabeş-Bolyai University of Cluj-NapocaRomania

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