Optimization of the interval type-2 fuzzy C-means using particle swarm optimization

2013 World Congress on Nature and Biologically Inspired Computing, NaBIC 2013

Volumn , Issue , 2013, Pages 10-15

  Rubio, E ^a   Castillo, O ^a  

^a TIJUANA INSTITUTE OF TECHNOLOGY   (Mexico)

Author keywords

Cluster validation; Interval type 2 fuzzy C means; Particle swarm optimization

Indexed keywords

CLUSTER VALIDATION; FUZZIFICATIONS; INTERVAL TYPE-2 FUZZY; OBJECTIVE FUNCTIONS; OPTIMAL NUMBER; OPTIMIZATION ALGORITHMS;

ALGORITHMS; COPYING; FUZZY CLUSTERING; FUZZY SYSTEMS;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 84887840881     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/NaBIC.2013.6617847     Document Type: Conference Paper

References (30)

1. J. Bezdek, "Pattern Recognition with Fuzzy Objective Function Algorithms", Plenum, 1981.
2. D. E. Gustafson and W. C. Kessel, "Fuzzy clustering with a fuzzy covariance matrix," in Proc. IEEE Conf. Decision Contr., San Diego, CA, pp. 761-766, 1979.
3. R. Krishnapuram and J. Keller, "A possibilistic approach to clustering," IEEE Trans. Fuzzy Sys., vol. I, no. 2, pp. 98-110, May 1993.
4. R. Krishnapuram and J. Keller, "The possibilistic c-Means algorithm: Insights and recommendations," IEEE Trans. Fuzzy Sys., vol. 4, no. 3, pp. 385-393, August 1996.
5. N. R. Pal, K. Pal, J. M. Keller and J. C. Bezdek, "A Possibilistic Fuzzy c-Means Clustering Algorithm, " IEEE Trans. Fuzzy Sys., vol. 13, no. 4, pp. 517-530, August 2005.
6. J. Yen; R. Langari; "Fuzzy Logic: Intelligence, Control, and Information," Upper Saddle River, New Jersey; Prentice Hall, 1999.
7. R. Kruse, C. Doring, M. J. Lesot; "Fundamentals of Fuzzy Clustering," In: Advances in Fuzzy Clustering and its Applications; John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO l 98SQ, England, 2007, Pages 3-30
8. K. Hirota, W. Pedrycz, "Fuzzy Computing for data mining, " Proceeding of the IEEE, Vol 87(9), 1999, pp 1575-1600.
9. N. S. Iyer, A. Kendel, and M. Schneider, "Feature-based fuzzy classification for interpretation of mamograms," Fuzzy Sets and Systems, Vol. 114, 2000, pp. 271-280.
10. W.E. Philips, R.P. Velthuinzen, S. Phuphanich, L.O. Hall, L.P Clark, and M. L Sibiger, "Aplication of fuzzy c-means segmentation technique for tissue deifferentation in MR images of hemorrhagic gliobastoma multi frome, " Magnetic Resonance Imaging, Vol 13 (2), 1995, pp. 277-290.
11. Miin-Shen Yang, Yu-Jen Hu, Karen Chia-Ren Lin, and Charles ChiaLee Lin, "Segmentation techniques for tissue differentiation in MRI of Ophthalmology using fuzzy clustering algorithms," Magnetic Resonance Imaging, Vol. 20, 2002, pp. 173-179.
12. X. Chang, Wei Li, and J. Farrell, "A C-means clustering based fuzzy modeling method," Fuzzy Systems, 2000. FUZZ IEEE 2000. The Ninth IEEE International Conference on, vol.2, 2000, pp. 937-940.
13. C. Hwang, F. Rhee, "Uncertain fuzzy clustering: interval type-2 fuzzy approach to C-means", IEEE Transactions on Fuzzy Systems 15 (1) (2007) 107-120.
14. B. Choi, F. Rhee, "interval type-2 fuzzy membership junction generation methods jor pattern recognition, " Information Sciences, Volume 179, Issue 13, 13 June 2009, Pages 2102-2122
15. L. A. Zadeh, 'The concept of a linguistic variable and its application to approximate reasoning-I," Inform. Sci., vol. 8, no. 3, pp. 199-249, 1975.
16. J. Mendel, "Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions," Prentice Hall, 2001.
17. K. L. Wu, M. S. Yang; A cluster validity index for fuzzy clustering, Pattern Recognition Letters, Volume 26, Issue 9, 1 July 2005, Pages 1275-1291.
18. M. K. Pakhira, S. Bandyopadhyay, U. Maulik, A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification, Fuzzy Sets and Systems, Volume 155, Issue 2, 16 October 2005, Pages 191-214.
19. W. Wang, Y. Zhang; On fuzzy cluster validity indices, Fuzzy Sets and Systems, Volume 158, Issue 19, Theme: Data Analysis, I October 2007, Pages 2095-2117, ISSN 0165-0114.
20. K. L. Wu, M. S. Yang, "A cluster validity index for fuzzy clustering", Pattern Recognition Letters, Volume 26, Issue 9, 1 July 2005, Pages 1275-1291.
21. Y. Zhang, W. Wang, X. Zhang, Y. Li, " A cluster validity index for fuzzy clustering", Information Sciences, Volume 178, Issue 4, 15 February 2008, Pages 1205-1218.
22. E. Rubio, O. Castillo, P. Melin; " A new validation index for fuzzy clustering and its comparisons with other methods". SMC 2011, pages 301-306.
23. R. Eberhart, J. Kennedy, "A new optimizer using particle swarm theory", in proc. 6th Int. Symp. Micro Machine and Human Science (MHS), Oct. 1995, pages: 39-43.
24. J. Kennedy, R. Eberhart, "Particle Swam Optimization", in Proc. IEEE Int. Conf. Neural Network (lCNN), Nov. 1995, vol. 4, pages: 1942-1948.
25. Y. del Valle, G.K. Venayagamoorthy, S. Mohagheghia J.-C. Hernandez and Harley R.G., "Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems ", Evolutionary Computation, IEEE Transactions on, Apr 2008, pages: 171-195.
26. R. Eberhart, Y. Shi and J. Kennedy, "Swam intelligence ", San Mateo, California. Morgan Kaufmann, 2001.
27. A. P. Engelbrecht, "Fundamentals of Computational Swarm Intelligence", John Wiley & Sons, 2006.
28. Escalante H. J., Montes M., Sucar L. E., "Particle Swarm Model Selection ", Journal of Machine Learning Research 10, 2009, pages: 405-440.
29. R. Eberhart, Y. Shi, "Particle swarm optimization: Developments, applications and resources ", in Procceding of the IEEE Congress on Evolutionary Computation, May 2001, vol. I, pages: 81-86.
30. F. Picard, "An introduction to mixture models ", Statics for Systems Biology, Research Report No. 7, March 2007.