Clustering fuzzy data using the fuzzy em algorithm

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6379 LNAI, Issue , 2010, Pages 333-346

  Quost, Benjamin ^a   Denœux, Thierry ^a  

^a UNIVERSITÉ DE TECHNOLOGIE DE COMPIÈGNE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING DATA; EM ALGORITHMS; FINITE MIXTURES; FUZZY DATA; FUZZY NUMBERS; GAUSSIAN MIXTURE MODEL; IMPRECISE DATA; MIXTURE MODEL; SYNTHETIC AND REAL DATA;

FUZZY SETS;

CLUSTERING ALGORITHMS;

EID: 77958044228     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15951-0_31     Document Type: Conference Paper

References (30)

1. Coppi, R., D'Urso, P.: Fuzzy K-means clustering models for triangular fuzzy time trajectories. Statistical Methods and Applications 11(1), 21-40 (2002)
2. Coppi, R., D'Urso, P.: Three-way fuzzy clustering models for LR fuzzy time tra-jectories. Computational Statistics and Data Analysis 43, 149-177 (2003)
3. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B 39, 1-38 (1977)
4. Denœux, T.: Maximum likelihood estimation from evidential data. In: Proceedings of the first workshop on the theory of belief functions and their applications, Brest, France (2010) (manuscript), http://www.ensieta.fr/ belief2010/
5. Denœux, T.: Maximum likelihood estimation from fuzzy data using the Fuzzy EM algorithm (working paper)
6. D'Urso, P., Giordani, P.: A weighted fuzzy c-means clustering model for fuzzy data. Computational Statistics and Data Analysis 50, 1496-1523 (2006) (Pubitemid 41617914)
7. Auephanwiriyakul, S., Keller, J.M.: Analysis and efficient implementation of a linguistic fuzzy c-means. IEEE Trans. on Fuzzy Systems 10(5), 563-582 (2002)
8. Hamdan, H., Govaert, G.: CEM algorithm for imprecise data. Application to flaw diagnosis using acoustic emission. In: Proc. of the IEEE International Conference on Systems, Man and Cybernetics, vol. (5), pp. 4774-4779. The Hague, Netherlands (2004)
9. Hamdan, H., Govaert, G.: Mixture model clustering of uncertain data. In: Proc. of the IEEE International Conference on Fuzzy Systems, Reno, Nevada, USA, pp. 879-884 (2005)
10. Hathaway, R.J., Bezdek, J.C., Pedrycz, W.: A parametric model for fusing hetero-geneous fuzzy data. IEEE Trans. on Fuzzy Systems 4(3), 1277-1282 (1996)
11. Hung, W.-L., Yang, M.-S.: Fuzzy clustering on LR-type fuzzy numbers with an application in Taiwanese tea evaluation. Fuzzy Sets and Systems 150(3), 561-577 (2005)
12. Gebhardt, J., Gil, M.A., Kruse, R.: Fuzzy set-theoretic methods in statistics. In: Slowinski, R. (ed.) Fuzzy Sets in Decision Analysis, Operations Research and Statistics, pp. 311-347. Kluwer Academic Publishers, Boston (1998)
13. Gil, M.A., Ĺopez-D́iaz, M., Ralescu, D.A.: Overview on the development offuzzy random variables. Fuzzy Sets and Systems 157(19), 2546-2557 (2006)
14. Jordan, M., Jacobs, R.: Hierarchical mixtures of experts and the EM algorithm. Neural Computation 6, 181-214 (1994)
15. Kruse, R., Meyer, K.D.: Statistics with vague data. Kluwer, Dordrecht (1987)
16. Mauris, G.: Expression of measurement uncertainty in a very limited knowledge context: a possibility theory-based approach. IEEE Trans. on Instrumentation and Measurement 56(3), 731-735 (2007)
17. McLachlan, G.J., Krishnan, T.: The EM Algorithm and Extensions. Wiley, New York (1997)
18. Pelekis, N., Iakovidis, D., Kotsifakos, E., Kopanakis, I.: Fuzzy Clustering of In-tuitionistic Fuzzy Data. International Journal of Business Intelligence and Data Mining 3(1), 45-65 (2008)
19. Redner, R., Walker, H.: Mixture densities, maximum likelihood and the EM algorithm. SIAM review 26(2), 195-239 (1984)
20. Sato, M., Sato, Y.: Fuzzy clustering model for fuzzy data. In: Proc. of the 4th IEEE Conf. on Fuzzy Systems, Yokohama, Japan, pp. 2123-2128 (1995)
21. Takata, O., Miyamoto, S., Umayahara, K.: Clustering of data with uncertainties using Hausdorff distance. In: Proc. of the 2nd IEEE International Conference on Intelligence Processing Systems, Gold Coast, Australia, pp. 67-71 (1998)
22. Takata, O., Miyamoto, S., Umayahara, K.: Fuzzy clustering of data with uncer-tainties using minimum and maximum distances based on L1 metric. In: Proc. of the Joint 9th IFSA World Congress and 20th NAFIPS International Conference, Vancouver, British Columbia, Canada, pp. 2511-2516 (2001)
23. Viertl, R.: Univariate statistical analysis with fuzzy data. Computational Statistics & Data Analysis 51(1), 133-147 (2006)
24. Wu, C.F.J.: On the convergence properties of the EM algorithm. Annals of Statis-tics 11, 95-103 (1983)
25. Xu, Z., Chen, J.: WU, J. Clustering algorithm for intuitionistic fuzzy sets. Infor-mation Sciences 178, 3775-3790 (2008)
26. Yang, M.S., Ko, C.H.: On a class offuzzy c-numbers clustering procedures for fuzzy data. Fuzzy Sets and Systems 84, 49-60 (1996)
27. Yang, M.S., Liu, H.H.: Fuzzy clustering procedures for conical fuzzy vector data. Fuzzy Sets and Systems 106, 189-200 (1999)
28. Yang, M.S., Hwang, P.Y., Chen, D.H.: Fuzzy clustering algorithms for mixed feature variables. Fuzzy Sets and Systems 141, 301-317 (2004)
29. Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338-353 (1965)
30. Zadeh, L.A.: Probability measures of fuzzy events. Journal of Mathematical Analysis and Applications 10, 421-427 (1968)