Clustering analysis method based on fuzzy C-means algorithm of PSO and PPSO with application in image data

  • Authors:

  • Jeng-Ming Yih;Yuan-Horng Lin;Hsiang-Chuan Liu

  • Affiliations:

  • Graduate Institute of Educational Measurement and Statistics, Department of Mathematics Education, National Taichung University, Taichung, Taiwan;Department of Mathematics Education, National Taichung University, Taichung, Taiwan;Department of Bioinformatics, Asia University, Taichung, Taiwan

  • Venue:
  • ACS'08 Proceedings of the 8th conference on Applied computer scince
  • Year:
  • 2008

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Abstract

The popular fuzzy c-means algorithm (FCM) converges to a local minimum of the objective function. Hence, different initializations may lead to different results. The important issue is how to avoid getting a bad local minimum value to improve the cluster accuracy. The particle swarm optimization (PSO) is a popular and robust strategy for optimization problems. But the main difficulty in applying PSO to real-world applications is that PSO usually need a large number of fitness evaluations before a satisfying result can be obtained. In this paper, the improved new algorithm, "Fuzzy C-Mean based on Picard iteration and PSO (PPSO-FCM)", is proposed. Two real data sets were applied to prove that the performance of the PPSO-FCM algorithm is better than the conventional FCM algorithm and the PSO-FCM algorithm.