A New Improved Fuzzy Possibilistic C-Means Algorithm Based on Weight Degree

  • Mohamed Fadhel SaadEmail author
  • Mohamed Adel Alimi
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 52)


Clustering (or cluster analysis) has been used widely in pattern recognition, image processing, and data analysis. It aims to organize a collection of data items into clusters, such that items within a cluster are more similar to each other than they are items in the other clusters. An improved fuzzy possibilistic clustering algorithm was developed based on the conventional fuzzy possibilistic c-means (FPCM) to obtain better quality clustering results. Numerical simulations show that the clustering algorithm gives more accurate clustering results than the FCM and FPCM methods.


Fuzzy C-means Fuzzy possibilistic C-means Modified fuzzy possibilistic C-means Possibilistic C-means 


  1. 1.
    Barni, M., Cappellini, V., & Mecocci, A. (1996). Comments on “A possibilistic approach to clustering”. IEEE Transactions on Fuzzy Systems, 4, 393–396.CrossRefGoogle Scholar
  2. 2.
    Borgelt, C. (2005). Prototype-based classification and clustering. Habilitation thesis, University of Magdeburg, Germany.Google Scholar
  3. 3.
    Blake, C.L., & Merz, C.J. (1998). UCI Repository of machine learning databases. University of California, Irvine, CA, USA.Google Scholar
  4. 4.
    Ruspini, E.R. (1969). A new approach to clustering. Information Control15(1), 22–32.zbMATHCrossRefGoogle Scholar
  5. 5.
    Timm, H., Borgelt, C., Doring, C., & Kruse, R. (2004). An extension to possibilistic fuzzy cluster analysis. Fuzzy Sets and Systems, 147(1), 3–16.MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Bezdek, J.C. (1981). Pattern recognition with fuzzy objective function algorithms. New York: Plenum.zbMATHCrossRefGoogle Scholar
  7. 7.
    Bezdek, J.C., Keller, J., Krishnapuram, R., & Pal, N.R. (1999). Fuzzy models and algorithms for pattern recognition and image processing. TA 1650.F89: Kluwer.Google Scholar
  8. 8.
    Dunn, J.C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3(3), 32–57.MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Lung, K. (2005). A cluster validity index for fuzzy clustering. Pattern Recognition Letters, 25, 1275–1291.Google Scholar
  10. 10.
    Zadeh, L. (1965). Fuzzy sets. Information Control, 8, 338–353.MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Pal, N.R., Pal, K., & Bezdek, J.C. (1977). A mixed c-means clustering model. Proceedings of the Sixth IEEE International Conference on Fuzzy Systems, 1, 11–21.CrossRefGoogle Scholar
  12. 12.
    Krishnapuram, R., & Keller, J. (1993). A possibilistic approch to clustering. IEEE Transactions on Fuzzy Systems, 1(2), 88–110.CrossRefGoogle Scholar
  13. 13.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (1996). Advances in knowledge discovery and data mining. Cambridge, MA: MIT.Google Scholar
  14. 14.
    Hung, W.L., Yang, M., & Chen, D. (2005). Parameter selection for suppressed fuzzy c-means with an application to MRI segmentation. Pattern Recognition Letters, 27(5), 424–438.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.Research Group on Intelligent MachinesUniversity of Sfax, ENISSfaxTunisia

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