Model-based cluster and discriminant analysis with the MIXMOD software
Tài liệu tham khảo
Banfield, 1993, Model-based Gaussian and non-Gaussian clustering, Biometrics, 49, 803, 10.2307/2532201
Bensmail, 1996, Regularized Gaussian discriminant analysis through eigenvalue decomposition, J. Amer. Statist. Assoc., 91, 1743, 10.2307/2291604
Biernacki, 1999, Choosing models in model-based clustering and discriminant analysis, J. Statist. Comput. Simulation, 64, 49, 10.1080/00949659908811966
Biernacki, 1999, An improvement of the NEC criterion for assessing the number of clusters in a mixture model, Pattern Recognition Lett., 20, 267, 10.1016/S0167-8655(98)00144-5
Biernacki, 2000, Assessing a mixture model for clustering with the integrated completed likelihood, IEEE Trans. Pattern Analysis and Machine Intelligence, 22, 719, 10.1109/34.865189
Biernacki, 2002, A generalized discriminant rule when training population and test population differ on their descriptive parameters, Biometrics, 58, 387, 10.1111/j.0006-341X.2002.00387.x
Biernacki, 2003, Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models, Comput. Statist. Data Anal., 41, 561, 10.1016/S0167-9473(02)00163-9
Bozdogan, 1993, Choosing the number of component clusters in the mixture-model using a new informational complexity criterion of the inverse-fisher information matrix, 40
Celeux, 1985, The SEM algorithm: a probabilistic teacher algorithm derived from the EM algorithm for the mixture problem, Comput. Statist. Quart., 2, 73
Celeux, 1992, A classification EM algorithm for clustering and two stochastic versions, Comput. Statist. Data Anal., 14, 315, 10.1016/0167-9473(92)90042-E
Celeux, 1995, Gaussian parsimonious clustering models, Pattern Recognition, 28, 781, 10.1016/0031-3203(94)00125-6
Celeux, 1996, An entropy criterion for assessing the number of clusters in a mixture model, J. Classification, 13, 195, 10.1007/BF01246098
Dempster, 1977, Maximum likelihood from incomplete data via the EM algorithm (with discussion), J. Roy. Statist. Soc. B, 39, 1
Diday, 1974, Classification avec distance adaptative, C. R. Acad. Sci. Paris, Sér. A, 278, 993
Fraley, 1998, How many clusters? Which clustering method? Answers via model-based cluster analysis, Comput. J., 41, 578, 10.1093/comjnl/41.8.578
Friedman, 1967, On some invariant criteria for grouping data, J. Amer. Statist. Assoc., 62, 1159, 10.2307/2283767
Kéribin, 2000, Consistent estimation of the order of mixture models, Sankhyã Ser. A, 1, 49
Maronna, 1974, Multivariate clustering procedure with variable metrics, Biometrics, 30, 499, 10.2307/2529203
McLachlan, 1992
McLachlan, 1997
McLachlan, 2000
Schroeder, 1976, Analyse d’un mélange de distributions de probabilité de même type, Rev. Statist. Appl., 24, 39
Schwarz, 1978, Estimating the number of components in a finite mixture model, Ann. Statist., 6, 461, 10.1214/aos/1176344136
Scott, 1971, Clustering methods based on likelihood ratio criteria, Biometrics, 27, 387, 10.2307/2529003
Ward, 1963, Hierarchical grouping to optimize an objective function, J. Amer. Statist. Assoc., 58, 236, 10.2307/2282967