Fuzzy clustering using hybrid fuzzy c-means and fuzzy particle swarm optimization

Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. However FCM is sensitive to initialization and is easily trapped in local optima. Particle swarm optimization (PSO) is a stochastic global optimization tool which is used in many optimization problems. In this paper a hybrid fuzzy clustering method based on FCM and fuzzy PSO (FPSO) is proposed which make use of the merits of both algorithms. Experimental results show that our proposed method is efficient and can reveal encouraging results.

[1]  Thomas A. Runkler,et al.  Fuzzy Clustering by Particle Swarm Optimization , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[2]  Xiyu Liu,et al.  A Novel Fuzzy Clustering Based on Particle Swarm Optimization , 2007, 2007 First IEEE International Symposium on Information Technologies and Applications in Education.

[3]  Alioune Ngom,et al.  A simulated annealing approach to find the optimal parameters for fuzzy clustering microarray data , 2005, XXV International Conference of the Chilean Computer Science Society (SCCC'05).

[4]  Bao-Jiang Zhao,et al.  An Ant Colony Clustering Algorithm , 2007, 2007 International Conference on Machine Learning and Cybernetics.

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

[6]  J. Wu,et al.  A genetic fuzzy k-Modes algorithm for clustering categorical data , 2009, Expert Syst. Appl..

[7]  James C. Bezdek,et al.  Fuzzy mathematics in pattern classification , 1973 .

[8]  James C. Bezdek,et al.  Optimization of clustering criteria by reformulation , 1995, IEEE Trans. Fuzzy Syst..

[9]  Der-Bang Wu,et al.  Fuzzy C-Mean Clustering Algorithms Based on Picard Iteration and Particle Swarm Optimization , 2008, 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing.

[10]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .

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

[12]  J. Wade Davis,et al.  Statistical Pattern Recognition , 2003, Technometrics.

[13]  Chunguang Zhou,et al.  Fuzzy discrete particle swarm optimization for solving traveling salesman problem , 2004, The Fourth International Conference onComputer and Information Technology, 2004. CIT '04..

[14]  Tieli Sun,et al.  An efficient hybrid data clustering method based on K-harmonic means and Particle Swarm Optimization , 2009, Expert Syst. Appl..