[1] |
Jain, A. K.; Dubes, R. C.: Algorithms for clustering data. (1988) · Zbl 0665.62061 |

[2] |
Mclachlan, G. J.; Basford, K. E.: Mixture models: inference and applications to clustering. (1988) · Zbl 0697.62050 |

[3] |
Poulsen, C. S.: Mixed Markov and latent Markov modelling applied to brand choice behaviour. Int. J. Res. marketing 7, No. 1, 5-19 (1990) |

[4] |
G. Ridgeway, Finite discrete Markov process clustering, Technical Report MSR-TR-97-24, Microsoft Research, Redmond, WA, USA, September 1997. |

[5] |
P. Smyth, Probabilistic model-based clustering of multivariate and sequential data, in: Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA, 4--6 January 1999, pp. 299--304. |

[6] |
I.V. Cadez, S. Gaffney, P. Smyth, A general probabilistic framework for clustering individuals and objects, in: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, USA, 20--23 August 2000, pp. 140--149. |

[7] |
I. Cadez, D. Heckerman, C. Meek, P. Smyth, S. White, Model-based clustering and visualization of navigation patterns on a web site, Technical Report MSR-TR-00-18, Microsoft Research, Redmond, WA, USA, March 2000; Revised September 2001. |

[8] |
I. Cadez, D. Heckerman, C. Meek, P. Smyth, S. White, Visualization of navigation patterns on a web site using model-based clustering, in: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, USA, 20--23 August 2000, pp. 280--284. |

[9] |
Dempster, A. P.; Laird, N. M.; Rubin, D. B.: Maximum likelihood from incomplete data via the EM algorithm (with discussion). J. R. Statist. soc. Ser. B 39, 1-38 (1977) · Zbl 0364.62022 |

[10] |
P. Sebastiani, M. Ramoni, P. Cohen, J. Warwick, J. Davis, Discovering dynamics using Bayesian clustering, in: Proceedings of the Third International Symposium on Intelligent Data Analysis, Amsterdam, Netherlands, 9--11 August 1999, pp. 199--209. |

[11] |
M. Ramoni, P. Sebastiani, P. Cohen, Multivariate clustering by dynamics, in: Proceedings of the 17th National Conference on Artificial Intelligence, Austin, TX, USA, 30 July--3 August 2000, pp. 633--638. |

[12] |
Ramoni, M.; Sebastiani, P.; Cohen, P.: Bayesian clustering by dynamics. Mach learning 47, No. 1, 91-121 (2002) · Zbl 1012.68154 |

[13] |
Kullback, S.; Leibler, R. A.: On information and sufficiency. Ann. math. Statist. 22, 79-86 (1951) · Zbl 0042.38403 |

[14] |
Rabiner, L. R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77, No. 2, 257-286 (1989) |

[15] |
L.R. Rabiner, C.H. Lee, B.H. Juang, J.G. Wilpon, HMM clustering for connected word recognition, in: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 1, Glasgow, UK, 23--26 May 1989, pp. 405--408. |

[16] |
Oates, T.; Firoiu, L.; Cohen, P. R.: Using dynamic time warping to bootstrap HMM-based clustering of time series. Sequence learning: paradigms, algorithms, and applications, 35-52 (2001) |

[17] |
Krogh, A.; Brown, M.; Mian, I. S.; Sjölander, K.; Haussler, D.: Hidden Markov models in computational biologyapplications to protein modeling. J. mol. Biol. 235, No. 5, 1501-1531 (1994) |

[18] |
Owsley, L. M. D.; Atlas, L. E.; Bernard, G. D.: Self-organizing feature maps and hidden Markov models for machine-tool monitoring. IEEE trans. Signal process. 45, No. 11, 2787-2798 (1997) |

[19] |
P. Smyth, Clustering sequences with hidden Markov models, in: Advances in Neural Information Processing Systems, Vol. 9, MIT Press, Cambridge, MA, 1997, pp. 648--654. |

[20] |
C. Li, G. Biswas, A Bayesian approach to temporal data clustering using hidden Markov models, in: Proceedings of the 17th International Conference on Machine Learning, Stanford, CA, USA, 29 June--2 July 2000, pp. 543--550. |

[21] |
M.P. Perrone, S.D. Connell, K-means clustering for hidden Markov models, in: Proceedings of the Seventh International Workshop on Frontiers in Handwriting Recognition, Amsterdam, Netherlands, 11--13 September 2000, pp. 229--238. |

[22] |
M.H. Law, J.T. Kwok, Rival penalized competitive learning for model-based sequence clustering, in: Proceedings of the 15th International Conference on Pattern Recognition, Vol. 2, Barcelona, Spain, 3--7 September 2000, pp. 195--198. |

[23] |
Desarbo, W. S.; Cron, W. L.: A maximum likelihood methodology for clusterwise linear regression. J. classification 5, No. 1, 249-282 (1988) · Zbl 0692.62052 |

[24] |
S. Gaffney, P. Smyth, Trajectory clustering with mixtures of regression models, in: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, 15--18 August 1999, pp. 63--72. |

[25] |
S. Gaffney, P. Smyth, Curve clustering with random effects regression mixtures, in: Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, FL, USA, 3--6 January 2003. |

[26] |
Box, G. E. P.; Jenkins, G. M.: Time series analysis: forecasting and control. (1970) · Zbl 0249.62009 |

[27] |
Box, G. E. P.; Jenkins, G. M.; Reinsel, G. C.: Time series analysis: forecasting and control. (1994) · Zbl 0858.62072 |

[28] |
H.Y. Kwok, C.M. Chen, L. Xu, Comparison between mixture of ARMA and mixture of AR model with application to time series forecasting, in: Proceedings of the Fifth International Conference on Neural Information Processing, Kitakyushu, Japan, 21--23 October 1998, pp. 1049--1052. |

[29] |
Ramoni, M. F.; Sebastiani, P.; Kohane, I. S.: Cluster analysis of gene expression dynamics. Proc. natl. Acad. sci. 99, No. 14, 9121-9126 (2002) · Zbl 1023.62110 |

[30] |
K. Kalpakis, D. Gada, V. Puttagunta, Distance measures for effective clustering of ARIMA time-series, in: Proceedings of the IEEE International Conference on Data Mining, San Jose, CA, USA, 29 November--2 December 2001, pp. 273--280. |

[31] |
Y. Xiong, D.Y. Yeung, Mixtures of ARMA models for model-based time series clustering, in: Proceedings of the IEEE International Conference on Data Mining, Maebashi City, Japan, 9--12 December 2002, pp. 717--720. |

[32] |
Redner, R. A.; Walker, H. F.: Mixture densities, maximum likelihood and the EM algorithm. SIAM rev. 26, No. 2, 195-239 (1984) · Zbl 0536.62021 |

[33] |
Celeux, G.; Diebolt, J.: The SEM algorithma probabilistic teacher algorithm derived from the EM algorithm for the mixture problem. Comput. statist. Quar. 2, 73-82 (1985) |

[34] |
Celeux, G.; Govaert, G.: A classification EM algorithm for clustering and two stochastic versions. Comput. statist. Data anal. 14, 315-332 (1992) · Zbl 0937.62605 |

[35] |
Stone, M.: Cross-validatory choice and assessment of statistical predictions. J. R. Statist. soc. Ser. B 36, No. 1, 111-147 (1974) · Zbl 0308.62063 |

[36] |
Stone, M.: Asymptotics for and against cross-validation. Biometrika 64, No. 1, 29-35 (1977) · Zbl 0368.62046 |

[37] |
P. Smyth, Model selection for probabilistic clustering using cross-validated likelihood, Technical Report 98-09, Department of Information and Computer Science, University of California, Irvine, CA, USA, February 1998. |

[38] |
Banfield, J. D.; Raftery, A. E.: Model-based Gaussian and non-Gaussian clustering. Biometrics 49, 803-821 (1993) · Zbl 0794.62034 |

[39] |
Kass, R. E.; Raftery, A. E.: Bayes factors. J. am. Statist. assoc. 90, 773-795 (1995) · Zbl 0846.62028 |

[40] |
Schwartz, G.: Estimating the dimension of a model. Ann. statist. 6, No. 2, 461-464 (1978) · Zbl 0379.62005 |

[41] |
Ljung, L.: System identification toolbox user’s guide. (2000) |

[42] |
M. Gavrilov, D. Anguelov, P. Indyk, R. Motwani, Mining the stock market: which measure is best? in: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, USA, 20--23 August 2000, pp. 487--496. |

[43] |
Keirn, Z. A.; Aunon, J. I.: A new mode of communication between man and his surroundings. IEEE trans. Biomed. eng. 37, No. 12, 1209-1214 (1990) |

[44] |
S. Zhong, J. Ghosh, HMMs and coupled HMMs for multi-channel EEG classification, in: Proceedings of the 2002 International Joint Conference on Neural Networks, Hawaii, USA, 12--17 May 2002, pp. 1154--1159. |