Fuzzy Clustering as a Data-Driven Development Environment for Information Granules

Handbook of Granular Computing

Volumn , Issue , 2008, Pages 153-169

  Fazendeiro, Paulo ^a   Valente De Oliveira, José ^b  

^a UNIVERSITY OF BEIRA INTERIOR   (Portugal)

^b UNIVERSITY OF ALGARVE   (Portugal)

Author keywords

alternating cluster estimation (ACE) framework; Cluster validity analysis; Fuzzy clustering essentials; Fuzzy set information granulation framework; Granular computing (GrC) and general computation theory; Granular computing and data algorithmic abstraction; Information granule fuzzy clustering formation; Possibilistic C means clustering algorithm

Indexed keywords

EID: 84889477542     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470724163.ch7     Document Type: Chapter

References (69)

1. Y. Yao. Three perspectives of granular computing. In: Proceedings of the International Forum on Theory of GrCfrom Rough Set Perspective, Journal of Nanchang Institute of Technology, Nanchang, China, Vol. 25, no. 2, 2006, pp. 16-21.
2. L. Zadeh. Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst. 90 (1997) 111-127.
3. L. Zadeh. Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems. Soft Comput. 2(1) (1998) 23-25.
4. K. Hirota and W. Pedrycz. Fuzzy computing for data mining. Proc. IEEE 87(9) (1999) 1575-1600.
5. A. Bargiela and W. Pedrycz. The roots of granular computing. In: Proceedings of the 2006 International Conference on Granular Computing, Allanta, USA, May 10-12, 2006, pp. 806-809.
6. Y. Yao. Perspectives of Granular Computing. In: Proceedings of2005 IEEE International Conference on Granular Computing, Beijing, China, July 25-27, 2005, Vol. 1, pp. 85-90.
7. W. Pedrycz. Conditional fuzzy clustering in the design of radial basis function neural network. IEEE Trans. Neural Netw. 9 (1998) 601-612.
8. A. Jain, M. Murty, and P. Flynn. Data clustering: a review. ACM Comput. Surv. 31(3) (1999) 264-323.
9. R. Xu and D. Wunsch II. Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3) (2005) 645-678.
10. R. Sibson. Slink: an optimally efficient algorithm for a complete link method. Comput. J. 16 (1973) 30-34.
11. D. Defays. An efficient algorithm for a complete link method. Comput. J. 20 (1977) 364-366.
12. E. Voorhees. Implementing agglomerative hierarchic clustering algorithms for use in document retrieval. Inf. Process. Manage. 22(6) (1986) 465-176.
13. G. Liu. Introduction to Combinatorial Mathematics. McGraw-Hill, New York, 1968.
14. E. Forgy. Cluster analysis of multivariate data: efficiency vs. interpretability of classifications. Biometrics 21 (1965) 768-780.
15. J. MacQueen. Some Methods for Classification and Analysis of Multivariate Observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, University of California Press, Berkeley, 1967, pp. 281-297.
16. L. Zadeh. Fuzzy sets. Inf. Control 8 (1965) 338-353.
17. E. Ruspini. A new approach to clustering. Inf. Control 15 (1969) 22-32.
18. J. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York, 1981.
19. F. Klawonn and F. Hoppner. What is fuzzy about fuzzy clustering? Understanding and improving the concept of the fuzzifier. Lect. Notes Comput. Sei. 2810 (2003) 254-264.
20. R. Hathaway, J. Bezdek, and Y. Hu. Generalized fuzzy c-means clustering strategies using L_inf_p_/inf_ norm distances. IEEE Trans. Fuzzy Syst. 8(5) (2000) 576-582.
21. J. Bezdek, R. Hathaway, M. Sabin, and W. Tucker. Convergence theory for fuzzy c-means: counterexamples and repairs. IEEE Trans. Syst. Man Cybern. 17 (1987) 873-877.
22. R. Krishnapuram and J. Keller. A possibilistic approach to clustering. IEEE Trans. Fuzzy Syst. 1 (1993) 98-110.
23. R. Krishnapuram and J. Keller. The possibilistic C-means algorithm: insights and recommendations. IEEE Trans. Fuzzy Syst. 4 (1996) 385-393.
24. J. Valente de Oliveira and W. Pedrycz (eds.) . Advances in Fuzzy Clustering and Its Applications, Wiley, New York, 2007.
25. D. Gustafson and W. Kessel. Fuzzy clustering with a fuzzy covariance matrix. In: Proceedings of the IEEE Conference on Decision Control, San Diego, CA, 1979, pp. 761-766.
26. I. Gath and A. Geva. Unsupervised optimal fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 11 (1989) 773-781.
27. J. Bezdek, C. Coray, R. Gunderson, and J. Watson. Detection and characterization of cluster substructure, I: Linear structure: fuzzy c-lines. J. Appl. Math. 40(2) (1981) 339-357.
28. J. Bezdek, C. Coray, R. Gunderson, and J. Watson. Detection and characterization of cluster substructure, II Fuzzy c-varieties and convex combinations thereof. J. Appl. Math. 40(2) (1981) 358-372.
29. R. Dave. Fuzzy shell clustering and application to circle detection in digital images. Int. J. Gen. Syst. 16 (1990) 343-355.
30. R. Krisnapuram, H. Frigui, and O. Nasroui. Fuzzy and possibilistic shell clustering algorithms and their application to boundary detection and surface approximation. IEEE Trans. Fuzzy Syst. 3(1) (1995) 29-60.
31. F. Hoppner. Fuzzy shell clustering algorithms in image processing fuzzy c-rectangular and two rectangular shells. IEEE Trans. Fuzzy Syst. 5 (1997) 599-613.
32. R. Hathaway and J. Bezdek. Switching regression models and fuzzy clustering. IEEE Trans. Fuzzy Syst. 1 (1993) 195-204.
33. F. Hoppner, F. Klawonn, R. Krase, and T. Runkler. Fuzzy Cluster Analysis. John Wiley, Chichester, England, 1999.
34. Y. Ohashi. Fuzzy clustering and robust estimation. In: Proceedings of 9th Meeting SAS User Group Int, FL, 1984.
35. R. Dave. Characterization and detection of noise in clustering. Pattern Recognit. Lett. 12 (1991) 657-664.
36. R. Dave and S. Sen. On generalising the noise clustering algorithms. In: Proceedings of the 7th IFSA World Congress, IFSA'97, Prague, Czech Republic, June 25-29. 1997, pp. 205-210.
37. T. Runkler and J. Bezdek. Alternating cluster estimation: A new tool for clustering and function approximation. IEEE Trans. Fuzzy Syst. 7 (1999) 377-393.
38. M. Egan. Locating clusters in noisy data: a genetic fuzzy c-means clustering algorithm. In: Proceedings of the 1998 Conference of the North American Fuzzy Information Processing Society, IEEE, 1998, pp. 178-182.
39. M. Egan, M. Krishnamoorthy, and K. Rajan. Comparative study of a genetic fuzzy c-means algorithm and a validity guided fuzzy c-means algorithm for locating clusters in noisy data. In: Proceedings of the International Conference on Evolutionary Computation, 1998, Anchorage, USA, May 4-9, pp. 440-445.
40. L. Hall, B. Ozyurt, and J. Bezdek. Clustering with a genetically optimized approach. IEEE Trans. Evol. Comput. 3 (2) (1999) 103-112.
41. F. Klawonn and A. Keller. Fuzzy clustering with evolutionary algorithms. Int. J. Intell. Syst. 13(10/11) (1998) 975-991.
42. J. Liu and W. Xie. A genetics-based approach to fuzzy clustering. In: Proceedings of the 1995 IEEE International Conference on Fuzzy Systems, Vol. 4, Yokohama, Japan, March 20-24, 1995, pp. 2233-2240.
43. C.-H. Wei and C.-S. Fahn. A distributed approach to fuzzy clustering by genetic algorithms. In: Proceedings of the 1996 Asian Fuzzy Systems Symposium, IEEE, 1996, pp. 350-357.
44. M. Sarkar. Evolutionary programming-based fuzzy clustering. In: Proceedings of the Fifth Annual Conference on Evolutionary Programming. MIT Press, MA, 1996, pp. 247-254.
45. B. Yuan, G. Klir, and J. Swan-Stone. Evolutionary fuzzy c-means clustering algorithm. In: Proceedings of 1995 IEEE International Conference on Fuzzy Systems, Yokohama, Japan, March 20-24, Vol. 4, 1995, pp. 2221-2226.
46. T. Runkler. Ant colony optimization of clustering models. Int. J. Intell. Syst. 20(12) (2005) 1233-1261.
47. T. Runkler and C. Katz. Fuzzy clustering by particle swarm optimization. In: Proceedings of the 2006 IEEE International Conference on Fuzzy Systems, Vancouver, Canada July 16-21, 2006, pp. 601-608.
48. C. Fralley and A. Raftery. How many clusters? Which clustering method? Answers via model-based cluster analysis. Comput. J. 41(8) (1998) 578-588.
49. M. Halkidi, Y. Batistakis, and M. Vazirgiannis. Clustering algorithms and validity measures. In: Proceedings of the 13th International Conference on Scientific and Statistical Database Management, Fairfax, USA, July 18-20, 2001, pp. 3-22.
50. U. Maulik and S. Bandyopadhyay. Performance evaluation of some clustering algorithms and validity indices. IEEE Trans. Pattern Anal. Mach. Intell. 24(12) (2002) 1650-1654.
51. G. Milligan and C. Cooper. An examination of procedures for determining the number of clusters in a data set. Psychometrika 50(2) (1985) 159-179.
52. N. Pal and J. Bezdek. On cluster validity for the fuzzy c-means model. IEEE Tran. Fuzzy Syst. 3(3) (1995) 370-379.
53. X. Xie and G. Beni. A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 13 (1991) 841-847.
54. M. Halkidi, Y. Batistakis, and M. Vazirgiannis. On clustering validation techniques. J. Intell. Inf. Syst. Kluwer Publishers, Dordrecht, 17(2/3) (2001) 107-145.
55. H. Frigui and R. Krishnapuram. Clustering by competitive agglomeration, Pattern Recognit. 30(7) (1997) 1109-1119.
56. H. Frigui and R. Krishnapuram. A robust competitive clustering algorithm with applications in computer vision. IEEE Trans. Pattern Anal. Mach. Intell. 21(5) (1999) 450-465.
57. A. Lorette, X. Descombes, and J. Zerabia. Fully unsupervised fuzzy clustering with entropy criterion. In: Proceedings of the 15th International Conference on Pattern Recognition, Barcelona, Spain, September 3-8, Vol. 3, 2000, pp. 986-989.
58. A. Geva. Hierarchical unsupervised fuzzy clustering. IEEE Trans. Fuzzy Syst. 7(6) (1999) 723-733.
59. U. Maulik and S. Bandyopadhyay. Fuzzy partitioning using a real-coded variable-length genetic algorithm for pixel classification. IEEE Trans. Geosci. Remote Sens. 41(5) (2003) 1075-1081.
60. L. Zadeh. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybern. SMC-3(1) (1973) 28-14.
61. L. Zadeh. Fuzzy logic = computing with words. IEEE Trans. Fuzzy Syst. 4(2) (1996) 103-111.
62. L. Zadeh. The concept of linguistic variable and its application to approximate reasoning. Inf. Sei. 8 (1975) 199-249 (part I) , 301-357 (part II) .
63. L. Zadeh. The concept of linguistic variable and its application to approximate reasoning. Inf. Sei. 9 (1976) 43-80 (part III) .
64. J. Valente de Oliveira. Semantic constraints for membership function optimization. IEEE Trans. Syst. Man Cybern. Part A Syst. Man 29(1) (1999) 128-138.
65. G. Miller. The magic number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63 (1956) 81-97.
66. W. Pedrycz and G. Vukovich. Abstraction and specialization of information granules. IEEE Trans. Syst. Man Cybern. Part B 31(1) (2001) 106-111.
67. G. Castellano, A. Fanelli, and C. Mencar. Generation of interpretable fuzzy granules by a double-clustering technique. Arch. Control Sei. Spec. Issue Granular Comput. 12(4) (2002) 397-410.
68. A. Bargiela and W. Pedrycz. A model of granular data: a design problem with the Tchebyshev FCM. Soft Comput. 9 (2005) 155-163.
69. A. Bargiela, and W. Pedrycz. Recursive information granulation: aggregation and interpretation issues. IEEE Trans. Syst. Man Cybern. 33(1) (2003) 96-112.