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

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

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.

Keywords

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

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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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