Fuzzy C-means algorithm based on pso and mahalanobis distance

International Journal of Innovative Computing, Information and Control

Volumn 5, Issue 12, 2009, Pages 5033-5040

  Liu, Hsiang Chuan ^a   Yih, Jeng Ming ^b   Lin, Wen Chih ^a   Liu, Tung Sheng ^c  

^a ASIA UNIVERSITY   (Taiwan)

^b NATIONAL TAICHUNG UNIVERSITY OF EDUCATION   (Taiwan)

^c Science Center of Military Police Comnmnand   (Taiwan)

Author keywords

Fuzzy C means algorithm; Mahalanobis distance; PSO FCM algorithm

Indexed keywords

EUCLIDEAN DISTANCE; FCM ALGORITHM; FUZZY C-MEANS ALGORITHMS; G-K ALGORITHM; GUSTAFSON-KESSEL; M-ALGORITHMS; MAHALANOBIS; MAHALANOBIS DISTANCES; NON-SPHERICAL; PRIOR INFORMATION; REAL DATA SETS; SINGULARITY PROBLEMS;

COPYING; COVARIANCE MATRIX; FUZZY CLUSTERING; FUZZY RULES; FUZZY SYSTEMS; PARTICLE SWARM OPTIMIZATION (PSO);

CLUSTERING ALGORITHMS;

EID: 73449117292     PISSN: 13494198     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (21)

1. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Pleri urn, Kiuwer Acadernic, N.Y., 1981.
2. N. R. Pal, K. Pal and J. C. Bezdek, A possibilistic approach to clustering, IEEE Transactions on Fuzzy Systems, vol.1, no.2, pp.98-110, 1993.
3. N. R. Pal, K. Pal and J. C. Bezdek, A mixed c-mean clustering model, Proc. of the Siith IEEE International Conference on Fuzzy Systems, vol.1, pp.11-22, 1997.
4. D. E. Gustafson and W. C. Kessel, Proc. IEEE Conf. Decision Contr, San Diego, CA, 761 ,19?9.
5. A. Gath and B. Geva, Unsupervised optimal fuzzy clustering, IEEE Trans. Pattern Anal. Machine Intell., vol.11, pp.773-781, 1989.
6. J. C. Dunn, A fuzzy relative of tile isodata process and its use in detecting compact, well-separated clustersJ. Cybern., vol.3, pp.32-57, 1973.
7. H. C. Liu, J. M. Yih, T. W. Sheu and S. W. Liu, A new fuzzy possibility clustering algorithms based on unsupervised Mahalanobis distances, Proc. of the International Conference on Machine Learning and Cybernetics, vol.7, no.7, pp.3939-3944, 2007.
8. H. C. Liu, J. M. Yih, D. B. Wu and S. W. Liu, Fuzzy C-mean algorithm based on 'Completez" Mahalanobis distances, Proc. of the International Conference on Machine Learning and Cybernetics, vol.7, no.6, pp.3569-3574, 2008.
9. H. C. Liu, J. M. Yih and S. W. Liu, Fuzzy C-mean algorithm based on Mahalanobis distances and better initial values, Proc. of the 10th Joint Conference & 12th International Conference on Fuzzy Theory & Technology, vol.1, pp.1398-1404, 2007.
10. Y. Shi and R. Eberhart, Empirical study of paryical swarm optimization, Congress on Evolutionary Computation, pp.1945-1950, 1999.
11. J. Z. Huang, M. K. Ng, H. Rong and Z. Li, Automated variable weighting in K-means type clustering, IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.27, no.5, pp.657, 2005.
12. L. Jing, K. Ng Michael and J. Huang, An entropy weighting k-means algorithm for subspace clustering of high dimensional sparse data, IEEE Transaction on Knowledge and Data Engineering, vol.19, no.8, pp.1026-1041, 2007.
13. R. A. Fisher, The use of multiple measurements in taxonomic problems, Annals of Eugenics, vol.7, pp.179-188, 1936.
14. S. Wang and J. Watada, Studying distribution functions of fuzzy random variables and its applications to critical value functions, International Journal of Innovative Computing Information and Control, vol.5, no.2, pp.279-292, 2009.
15. H. Morita, E. Uchino and T. Yamakawa, Markov model based adaptive web advertisemeut system by tracking a user's taste, International Journal of Innovative Computing Information and Control, vol.5, no.3, pp.811-819, 2009.
16. L. Zhao, M. Liu and L. Qu, Disruption coordination of closed-loop supply chain network (II) Analysis and simulations, International Journal of Innovative Computing Information and Control, vol.5, no.2, pp.511-520, 2009.
17. C. C. Lai, C. H. Wu and M.-C. Tsai, Feature selection using particle swarm optimization with application in Spam filtering, International Journal of Innovative Computing Information and Control, vol.5, no.2, pp./123-432, 2009.
18. Y. Takeuchi and A. Hirata, A method of optimization of linear observations for the Kalman filter based on generalized water filling theorem International Journal of innovative Computing Information and Control, vol.5, no.1, pp.75-85, 2009.
19. Z. Zhang, G. Yang, J. Yi, Y. Zhu and Z. Tang, A new stochastic dynamic adaptive local search algorithm for Elman neural network, International Journal of Innovative Computing Information and Control, vol.4, no.11, pp.2927-2939, 2008.
20. B. Balasko, J. Abonyi and B. Feil, Fuzzy Clustering and Data Analysis Toolboi For Use with Matlab, http://www.mathworks.com/matlabcentral/ fileexchange/7473.
21. C. H. Li, W. C. Huang, B. C. Kuo and C. C. Hung, A novel fuzzy weighted C-method for image classification, International Journal of Fuzzy Systems, vol.10, no.3, pp.168-173, 2008.