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

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Intelligent Automation and Computer Engineering

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 52))

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Abstract

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.

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References

  1. Barni, M., Cappellini, V., & Mecocci, A. (1996). Comments on “A possibilistic approach to clustering”. IEEE Transactions on Fuzzy Systems, 4, 393–396.

    Article  Google Scholar 

  2. Borgelt, C. (2005). Prototype-based classification and clustering. Habilitation thesis, University of Magdeburg, Germany.

    Google Scholar 

  3. Blake, C.L., & Merz, C.J. (1998). UCI Repository of machine learning databases. University of California, Irvine, CA, USA.

    Google Scholar 

  4. Ruspini, E.R. (1969). A new approach to clustering. Information Control15(1), 22–32.

    Article  MATH  Google Scholar 

  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.

    Article  MathSciNet  MATH  Google Scholar 

  6. Bezdek, J.C. (1981). Pattern recognition with fuzzy objective function algorithms. New York: Plenum.

    Book  MATH  Google Scholar 

  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. 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.

    Article  MathSciNet  MATH  Google Scholar 

  9. Lung, K. (2005). A cluster validity index for fuzzy clustering. Pattern Recognition Letters, 25, 1275–1291.

    Google Scholar 

  10. Zadeh, L. (1965). Fuzzy sets. Information Control, 8, 338–353.

    Article  MathSciNet  MATH  Google Scholar 

  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.

    Article  Google Scholar 

  12. Krishnapuram, R., & Keller, J. (1993). A possibilistic approch to clustering. IEEE Transactions on Fuzzy Systems, 1(2), 88–110.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Research Group on Intelligent Machines, University of Sfax, ENIS, BP W 3038, Sfax, Tunisia

    Mohamed Fadhel Saad & Mohamed Adel Alimi

Authors
  1. Mohamed Fadhel Saad
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  2. Mohamed Adel Alimi
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Corresponding author

Correspondence to Mohamed Fadhel Saad .

Editor information

Editors and Affiliations

  1. School of Information Sciences &, Engineering, University of Canberra, Canberra, 2601, Australia

    Xu Huang

  2. International Association of Engineers, Hung To Road 37-39, Hong Kong, Hong Kong/PR China

    Sio-Iong Ao

  3. Dept. Computer Science, Tijuana Institute of Technology, Chula Vista, USA

    Oscar Castillo

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Saad, M.F., Alimi, M.A. (2009). A New Improved Fuzzy Possibilistic C-Means Algorithm Based on Weight Degree. In: Huang, X., Ao, SI., Castillo, O. (eds) Intelligent Automation and Computer Engineering. Lecture Notes in Electrical Engineering, vol 52. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3517-2_7

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  • DOI: https://doi.org/10.1007/978-90-481-3517-2_7

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-3516-5

  • Online ISBN: 978-90-481-3517-2

  • eBook Packages: EngineeringEngineering (R0)

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