Fuzzy Clustering by Differential Evolution

A fuzzy clustering algorithm based on differential evolution (FCDE) is presented in this paper in order to overcome the disadvantages of traditional fuzzy c-means algorithm (FCM). FCM is sensitive to initialization so that its search is easy to fall into a local optimum. The algorithm we proposed in this paper will avoid this problem and lead to global optimum. The experiments show that FCDE has better performance than FCM and is more efficient particularly when the number of dimension of data becomes large.

[1]  Sandra Paterlini,et al.  Differential evolution and particle swarm optimisation in partitional clustering , 2006, Comput. Stat. Data Anal..

[2]  Mitsuo Gen,et al.  Genetic algorithm for fuzzy clustering , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[3]  Amit Konar,et al.  Automatic Fuzzy Segmentation of Images with Differential Evolution , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[4]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[5]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[6]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[7]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[8]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[9]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.