A method for training finite mixture models under a fuzzy clustering principle

Fuzzy Sets and Systems

Volumn 161, Issue 23, 2010, Pages 3000-3013

  Chatzis, Sotirios ^a  

^a UNIVERSITY OF MIAMI   (United States)

Author keywords

Fuzzy clustering; Fuzzy statistics and data analysis; Learning

Indexed keywords

BENCHMARK DATA; FINITE MIXTURE MODELS; FUZZY C-MEANS ALGORITHMS; FUZZY MODELS; FUZZY STATISTICS AND DATA ANALYSIS; LEARNING; PROBABILISTIC PROPERTIES;

BENCHMARKING; CLASSIFICATION (OF INFORMATION); CLUSTER ANALYSIS; FUZZY CLUSTERING; FUZZY SYSTEMS; MAXIMUM LIKELIHOOD; MIXTURES;

CLUSTERING ALGORITHMS;

EID: 77957879865     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fss.2010.03.015     Document Type: Article

References (19)

1. G. McLachlan, and D. Peel Finite Mixture Models 2000 Wiley Series in Probability and Statistics
2. A. Dempster, N. Laird, and D. Rubin Maximum likelihood from incomplete data via the EM algorithm Journal of the Royal Statistical Society B 39 1 1977 1 38
3. M. Tipping, and C. Bishop Mixtures of probabilistic principal component analyzers Neural Computation 11 2 1999 443 482
4. L. Zadeh Fuzzy sets Information and Control 8 1965 338 353
5. J. Bezdek Pattern Recognition with Fuzzy Objective Function Algorithms 1981 Plenum Press New York, London
6. J.C. Bezdek, N.R. Pal, J. Keller, and R. Krisnapuram Fuzzy Models and Algorithms for Pattern Recognition and Image Processing 1999 Kluwer Academic Publishers Norwell, MA, USA
7. K. Honda, and H. Ichihashi Regularized linear fuzzy clustering and probabilistic PCA mixture models IEEE Transactions on Fuzzy Systems 13 4 2005 508 516 (Pubitemid 41461445)
8. S. Miyamoto, K. Umayahara, Fuzzy clustering by quadratic regularization, in: Proc. of FUZZ-IEEE'98, Anchorage, Alaska, 1998.
9. I. Gath, and A.B. Geva Unsupervised optimal fuzzy clustering IEEE Transactions on Pattern Analysis and Machine Intelligence 11 7 1989 773 781
10. M.-T. Gan, M. Hanmandlu, and A.H. Tan From a Gaussian mixture model to additive fuzzy systems IEEE Transactions on Fuzzy Systems 13 3 2005 303 316 (Pubitemid 40914088)
11. R.J. Hathaway Another interpretation of the EM algorithm for mixture distributions Statistics & Probability Letters 4 1986 53 56
12. H. Ichihashi, K. Honda, N. Tani, Gaussian mixture pdf approximation and fuzzy c-means clustering with entropy regularization, in: Proc. Fourth Asian Fuzzy System Symp., 2000, pp. 217-221.
13. Z. Ghahramani, G. Hinton, The EM algorithm for mixtures of factor analyzers, Technical Report CRGTR-96-1, Department of Computer Science, University of Toronto, Toronto, Canada, 1997.
14. E. Ruspini A new approach to clustering Information and Control 15 1969 22 32
15. G. McLachlan, R. Bean, and L.B.-T. Jones Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution Computational Statistics & Data Analysis 51 11 2007 5327 5338 (Pubitemid 46694022)
16. C. Liu, and D. Rubin ML estimation of the t distribution using EM and its extensions ECM and ECME Statistica Sinica 5 1 1995 19 39
17. A. Asuncion, D. Newman, UCI machine learning repository, 2007. [Online]. Available: 〈 http://www.ics.uci.edu/ ∼ mlearn/MLRepository.html 〉.
18. N. Ueda, and R. Nakano Deterministic annealing em algorithm Neural Networks 11 2 1998 271 282 (Pubitemid 28231473)
19. X. Zhu, Z. Ghahramani, J. Lafferty, Semi-supervised learning using gaussian fields and harmonic functions, in: Proc. of the ICML, 2003.