Robust Clustering by Pruning Outliers

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 33, Issue 6, 2003, Pages 983-999

  Zhang, Jiang She ^a   Leung, Yiu Wing ^b  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b HONG KONG BAPTIST UNIVERSITY   (Hong Kong)

Author keywords

Deterministic annealing; Fuzzy; Possibility theory; Robust clustering

Indexed keywords

ANNEALING; FUZZY SETS; MAXIMUM LIKELIHOOD ESTIMATION; NORMAL DISTRIBUTION; PROBABILITY DENSITY FUNCTION; ROBUSTNESS (CONTROL SYSTEMS);

PRUNING OUTLIERS; ROBUST CLUSTERING;

CYBERNETICS;

EID: 0344827211     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2003.816993     Document Type: Article

References (56)

1. A. K. Jain and R. C. Dubes, Algorithms for Clustering Data. Englewood Cliffs, NJ: Prentice-Hall, 1988.
2. F. Hopper, F. Klawonn, R. Kruse, and T. Runkler, Fuzzy Cluster Analysis. New York: Wiley, 1999.
3. J. Wang, "A linear assignment clustering algorithm based on the least similar cluster representations," IEEE Trans. Syst. Man Cybern., vol. 29, pp. 100-104, Jan. 1999.
4. L. O. Hall, I. B. Ozyurt, and J. C. Bezdek, "Clustering with a genetically optimized approach," IEEE Trans. Evol. Comput., vol. 3, pp. 103-112, July 1999.
5. M. Setnes and R. Babuska, "Fuzzy relational classifier trained by fuzzy clustering," IEEE Trans. Syst. Man Cybern., vol. 29, pp. 619-625, Oct. 1999.
6. A. Baraldi and P. Blonda, "A survey of fuzzy clustering algorithms for pattern recognition - part I," IEEE Trans. Syst. Man Cybern., vol. 29, pp. 778-785, Dec. 1999.
7. _, "A survey of fuzzy clustering algorithms for pattern recognition - part II," IEEE Trans. Syst. Man Cybern., vol. 29, pp. 786-801, Dec. 1999.
8. A. K. Jain, R. P. W. Duin, and J. Mao, "Statistical pattern recognition: a review," IEEE Trans. Pattern Anal. Machine Intell., vol. 22, pp. 4-37, Jan. 2000.
9. H. Yan, "Fuzzy curve-tracing algorithm," IEEE Trans. Syst. Man Cybern. B, vol. 31, pp. 768-780, Oct: 2001.
10. J. Tian, "Better reliability assessment and prediction through data clustering," IEEE Trans. Software Eng., vol. 28, pp. 997-1007, Oct. 2002.
11. M. Zhang, L. O. Hall, and D. B. Goldgof, "A generic knowledge-guided image segmentation and labeling system using fuzzy clustering algorithms," IEEE Trans. Syst. Man Cybern. B, vol. 32, pp. 571-582, Oct. 2002.
12. C. H. Cheng, W. K. Lee, and K. F. Wong, "A genetic algorithm-based clustering approach for database partitioning," IEEE Trans. Syst. Man Cybern. B, vol. 32, pp. 215-230, Aug. 2002.
13. J. Gonzalez, I. Rojas, H. Pomares, J. Ortega, and A. Prieto, "A new clustering technique for function approximation," IEEE Trans. Neural Networks, vol. 13, pp. 132-142, Jan. 2002.
14. J. C. Bezdek, J. Keller, R. Krisnapuram, and N. R. Pal, Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Boston, MA: Kluwer, 1999.
15. R. Dubes and A. K. Jain, "Clustering techniques: The user's dilemma," Pattern Recogn., vol. 8, pp. 247-260, 1976.
16. R. Duda and P. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.
17. J. C. Bezdek, "Fuzzy Mathematics in Pattern Classification," Ph.D. dissertation, Cornell Univ., Ithaca, NY, 1973.
18. _, Pattern Recognition With Fuzzy Objective Function Algorithms. New York: Plenum, 1981.
19. J. C. Bezdek and S. K. Pal, Fuzzy Models for Pattern Recognition. New York: IEEE, 1992.
20. K. Rose, E. Gurewitz, and G. C. Fox, "A deterministic annealing approach to clustering," Pattern Recogn. Lett., vol. 11, pp. 589-594, 1990.
21. R. N. Dave and R. Krishnapuram, "Robust clustering methods: a unified review," IEEE Trans. Fuzzy Syst., vol. 5, pp. 270-293, May 1997.
22. R. N. Dave, "Characterization and detection of noise in clustering," Pattern Recogn. Lett., vol. 12, pp. 657-664, 1991.
23. X. Zhuang, Y. Huang, K. Palaniappan, and Y. Zhao, "Gaussian mixture density modeling, decomposition, and applications," IEEE Trans. Image Processing, vol. 5, pp. 1293-1302, Sept. 1996.
24. R. Krishnapuram and J. Keller, "A possibilistic approach to clustering," IEEE Trans. Fuzzy Systems, vol. 1, pp. 98-110, Feb. 1993.
25. _, "The possibilistic c-means algorithm: insights and recommendations," IEEE Trans. Fuzzy Systems, vol. 4, no. 3, pp. 385-393, 1996.
26. M. Barni, V. Cappellini, and A. Mecocci, "Comments on 'a possibilistic approach to clustering'," IEEE Trans. Fuzzy Systems, vol. 4, pp. 393-396, June 1996.
27. J. S. Zhang and Y. W. Leung, "Improved possibilistic C-Means clustering algorithms," Research Rep., Dept. Comput. Sci., Hong Kong Baptist Univ., Hong Kong, 2000.
28. H. Frigui and R. Krishnapuram, "A robust competitive clustering algorithm with applications in computer vision," IEEE Trans. Pattern Anal. Machine Intell., vol. 21, pp. 450-465, May 1999.
29. C. W. Wu, J. L. Chen, and J. H. Wang, "Self-organizing mountain method for clustering," in Proc. IEEE Int. Conf Syst. Man Cyber., 2001, pp. 2434-2438.
30. J. W. Lee, S. H. Son, and S. H. Kwon, "Advanced mountain clustering method," in Proc. Joint 9th IFSA World Congr. 20th NAFIPS Int. Conf, 2001, pp. 275-280.
31. J. C. Bezdek and N. R. Pal, "Some new indexes or cluster validity," IEEE Trans. Syst., Man, Cybern., vol. 28, no. 3, pp. 301-303, June 1998.
32. P. Wahlberg and G. Lantz, "Methods for robust clustering of epileptic EEG spikes," IEEE Trans. Biomed. Engg., vol. 47, pp. 857-868, July 2000.
33. K. Honda, N. Tongo, T. Fujii, and H. Ichihashi, "Linear fuzzy clustering based on least absolute divations," in Proc. IEEE Int. Conf. Fuzzy Syst., 2002, pp. 1444-1449.
34. Y. Yao, L. Chen, and Y. Q. Chen, "Associative clustering for clusters of arbitrary distribution shapes," Neural Processing Lett., vol. 14, no. 3, pp. 169-177, 2001.
35. K. Inui, S. Kaneko, and S. Igarashi, "Robust line fitting using LMedS clustering," Trans. Inst. Elect. Inform. Commun. Eng. D-II, vol. J84D-II, no. 1, pp. 48-55, 2001.
36. Y. Yao, Y. Q. Chen, and L. Chen, "A novel similarity measure for data clustering," Intell. Data Anal., vol. 4, no. 5, pp. 421-431, 2000.
37. S. Shoham, "Robust clustering by deterministic agglomeration EM of mixtures of multivariate t-distributions," Pattern Recogn., vol. 35, no. 5, pp. 1127-1142, 2002.
38. S. Sen and R. N. Dave, "Application of noise clustering in group technology," in Proc. 18th Int. Conf. North Amer. Fuzzy Inform. Processing Soc., New York, June 1999, pp. 366-370.
39. R. N. Dave and S. Sen, "Generalized noise clustering as a robust fuzzy c-M-estimators model," in Proc. 17th Annu. Conf. North Amer. Fuzzy Inform. Processing Soc., Florida, Aug. 1998, pp. 256-260.
40. E. E. Zhuk, "Robust cluster analysis of discrete multivariate observations," Automat. Remote Contr., vol. 62, no. 1, 2001.
41. R. J. Hathaway and J. C. Bezdek, "Fuzzy c-means clustering of incomplete data," IEEE Trans. Syst. Man Cybern. B, vol. 31, pp. 735-744, Oct. 2001.
42. R. N. Dave and S. Sen, "Robust fuzzy clustering of relational data," IEEE Trans. Fuzzy Syst., vol. 10, pp. 713-727, Dec. 2002.
43. E. E. Gustafson and W. C. Kessel, "Fuzzy clustering with a fuzzy covariance matrix," in Proc. IEEE Conf. Decision Contr., San Diego, CA, 1979, pp. 761-766.
44. R. Hathaway, J. Bezdek, and W. Tucker, "An improved convergence theory for the fuzzy c-means clustering algorithms," in Analysis of Fuzzy Information, J. C. Bezdek, Ed. Boca Raton, FL: CRC, 1987, vol. 3, pp. 123-132.
45. R. J. Hathaway and J. C. Bezdek, "Optimization of clustering criteria by reformulation," IEEE Trans. Fuzzy Syst., vol. 3, pp. 241-245, Apr. 1995.
46. J. C. Bezdek and N. R. Pal, "Two soft relatives of learning vector quantization," Neural Networks, vol. 8, no. 5, pp. 729-743, 1995.
47. C. Charalambous and J. W. Bandler, "Non-linear minimax optimization as a sequence of least pth optimization with finite value of p," Int. J. Syst. Sci., vol. 7, no. 4, pp. 377-391, 1976.
48. T. Martinetz, G. Berkovich, and K. Schulten, "Neural-gas network for vector quantization and its application to time-series predictions," IEEE Trans. Neural Networks, vol. 4, pp. 558-569, July 1993.
49. X. S. Li, "Aggregation function approach to nonlinear minimax problem," Comput. Struct. Mech. Appl., vol. 8, no. 1, pp. 85-91, 1991.
50. J. C. Bezdek, J. M. Keller, R. Krishnapuram, L. I. Kuncheva, and N. R. Pal, "Will the real Iris data please stand up?," IEEE Trans. Fuzzy Syst., vol. 7, pp. 368-369, June 1999.
51. N. B. Karayiannis, J. C. Bezdek, N. P. Pal, R. J. Hathaway, and P. I. Pai, "Repairs to GLVQ: a new family of competitive learning schemes," IEEE Trans. Neural Networks, vol. 7, pp. 1062-1071, Sept. 1996.
52. E. Yair, K. Zeger, and A. Gersho, "Competitive learning and soft competition for vector quantizer design," IEEE Trans. Signal Processing, vol. 40, pp. 294-309, Feb. 1992.
53. U. Maulik and S. Bandyopadhyay, "Performance evaluation of some clustering algorithms and validity indices," IEEE Trans. Pattern Anal. Machine Intell., vol. 24, pp. 1650-1654, Dec. 2002.
54. R. E. Hammah and J. H. Curran, "Validity measures for the fuzzy cluster analysis of orientations," IEEE Trans. Pattern Anal. Machine Intell., vol. 22, pp. 1467-1472, Dec. 2000.
55. U. Kaymak and M. Setnes, "Fuzzy clustering with volume prototypes and adaptive cluster merging," IEEE Trans. Fuzzy Syst., vol. 10, pp. 705-712, Dec. 2002.
56. M. Setnes and U. Kaymak, "Extended fuzzy c-means with volume prototypes and cluster merging," in Proc. 6th Eur. Congr. Intell. Tech. Soft Comput., vol. 2, Sept. 1998, pp. 1360-1364.