Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm

Abstract Fuzzy c-means clustering algorithm (FCM) is a method that is frequently used in pattern recognition. It has the advantage of giving good modeling results in many cases, although, it is not capable of specifying the number of clusters by itself. Aimed at the problems existed in the FCM clustering algorithm, a kernel-based fuzzy c-means (KFCM) is clustering algorithm is proposed to optimize fuzzy c-means clustering, based on the Genetic Algorithm (GA) optimization which is combined of the improved genetic algorithm and the kernel technique (GAKFCM). In this algorithm, the improved adaptive genetic algorithm is used to optimize the initial clustering center firstly, and then the KFCM algorithm is availed to guide the categorization, so as to improve the clustering performance of the FCM algorithm. In the paper, Matlab is used to realize the simulation, and the performance of FCM algorithm, KFCM algorithm and GAKFCM algorithm is testified by test datasets. The results proved that the GAKFCM algorithm proposed overcomes FCM׳s defects efficiently and improves the clustering performance greatly.

[1]  Dao-Qiang Zhang,et al.  A novel kernelized fuzzy C-means algorithm with application in medical image segmentation , 2004, Artif. Intell. Medicine.

[2]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[3]  Magne Setnes,et al.  Fuzzy relational classifier trained by fuzzy clustering , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[4]  Zhe Chen,et al.  Genetic algorithms encoding study and a sufficient convergence condition of GAs , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[5]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[6]  Luis Magdalena,et al.  Evolutionary-based learning applied to fuzzy controllers , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[7]  M.A. Lee,et al.  Integrating design stage of fuzzy systems using genetic algorithms , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[8]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[9]  Akif Asil Bulgak,et al.  Interpolation behavior of TS fuzzy controllers , 1999, Proceedings of the 1999 IEEE International Symposium on Intelligent Control Intelligent Systems and Semiotics (Cat. No.99CH37014).

[10]  Andrew R. Webb,et al.  Statistical Pattern Recognition , 1999 .

[11]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[12]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Mohamad T. Musavi,et al.  On the training of radial basis function classifiers , 1992, Neural Networks.

[14]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[15]  J.-S.R. Jang.  Fuzzy controller design without domain experts , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

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

[17]  Daoqiang Zhang,et al.  Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Plamen P. Angelov,et al.  A fuzzy controller with evolving structure , 2004, Inf. Sci..

[19]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[20]  Mitsuo Gen,et al.  Genetic algorithms and engineering design , 1997 .

[21]  Jyh-Shing Roger Jang,et al.  Self-learning fuzzy controllers based on temporal backpropagation , 1992, IEEE Trans. Neural Networks.

[22]  Hao Wang,et al.  Introduction to Genetic Algorithms in Electromagnetics , 1995 .

[23]  Daoqiang Zhang,et al.  Robust fuzzy relational classifier incorporating the soft class labels , 2007, Pattern Recognit. Lett..

[24]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

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

[26]  Isao Hayashi,et al.  NN-driven fuzzy reasoning , 1991, Int. J. Approx. Reason..

[27]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[28]  Rajendra Prasad,et al.  Application of GA and PSO Tuned Fuzzy Controller for AGC of Three Area Thermal- Thermal-Hydro Power System , 2010 .