A. Agresti, Categorical data analysis, 2002.

E. Allman, C. Matias, and J. Rhodes, Identifiability of parameters in latent structure models with many observed variables, Annals of Statistics, vol.37, issue.6A, pp.3099-3132, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00591202

J. Baudry and G. Celeux, EM for mixtures-initialization requires special care, Statistics and Computing, vol.25, issue.4, pp.713-726, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01256833

F. Beninel, C. Biernacki, C. Bouveyron, J. Jacques, and A. Lourme, Parametric link models for knowledge transfer in statistical learning. Knowledge Transfer: Practices, Types and Challenges, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00942660

C. Biernacki, F. Beninel, and V. Bretagnolle, A generalized discriminant rule when training population and test population differ on their descriptive parameters, Biometrics, vol.58, issue.2, pp.387-397, 2002.
URL : https://hal.archives-ouvertes.fr/hal-00191396

C. Biernacki, G. Celeux, and G. Govaert, Assessing a mixture model for clustering with the integrated completed likelihood, IEEE TPAMI, vol.22, issue.7, pp.719-725, 2000.

C. Biernacki, G. Celeux, and G. Govaert, Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate gaussian mixture models, Computational Statistics & Data Analysis, vol.41, issue.3, pp.561-575, 2003.

C. Biernacki, G. Celeux, G. Govaert, and F. Langrognet, Model-based cluster and discriminant analysis with the MIXMOD software, Computational Statistics & Data Analysis, vol.51, issue.2, pp.587-600, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00069878

C. M. Bishop, Pattern recognition and machine learning, vol.4, 2006.

D. M. Blei and J. D. Lafferty, Dynamic topic models, Proc. of the Int Conf on Machine Learning, pp.113-120, 2006.

D. M. Blei, A. Y. Ng, and M. I. Jordan, Latent Dirichlet allocation, Journal of Machine Learning Research, vol.3, pp.993-1022, 2003.

D. Chakrabarti, R. Kumar, and A. Tomkins, Evolutionary clustering, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.554-560, 2006.

Y. Chi, X. Song, D. Zhou, K. Hino, and B. L. Tseng, On evolutionary spectral clustering, ACM Trans. on Knowledge Discovery from Data, vol.3, issue.4, p.17, 2009.
DOI : 10.1145/1631162.1631165
URL : http://infolab.stanford.edu/~echang/ecml08-song.pdf

A. Dubey, A. Hefny, S. Williamson, and E. P. Xing, A nonparametric mixture model for topic modeling over time, SDM. SIAM, pp.530-538, 2013.
DOI : 10.1137/1.9781611972832.59
URL : http://arxiv.org/pdf/1208.4411

J. Ferlez, C. Faloutsos, J. Leskovec, D. Mladenic, and M. Grobelnik, Monitoring network evolution using MDL, IEEE Int. Conf. on Data Engineering, pp.1328-1330, 2008.
DOI : 10.1109/icde.2008.4497545

M. A. Figueiredo and A. K. Jain, Unsupervised learning of finite mixture models, IEEE TPAMI, vol.24, issue.3, pp.381-396, 2002.
DOI : 10.1109/34.990138

C. Fraley and A. E. Raftery, Model-based clustering, discriminant analysis, and density estimation, Journal of the American Statistical Association, vol.97, issue.458, pp.611-631, 2002.
DOI : 10.1198/016214502760047131

V. Garcia and F. Nielsen, Simplification and hierarchical representations of mixtures of Exponential families, Signal Processing, vol.90, issue.12, pp.3197-3212, 2010.

M. A. Hasnat, O. Alata, and A. Trémeau, Model-based hierarchical clustering with Bregman Divergences and Fishers mixture model: application to depth image analysis, Statistics and Computing, vol.26, issue.4, pp.861-880, 2016.
DOI : 10.1007/s11222-015-9576-3
URL : https://hal.archives-ouvertes.fr/hal-01220515

M. A. Hasnat, J. Velcin, S. Bonnevay, and J. Jacques, Simultaneous clustering and model selection for Multinomial distribution: A comparative study, Advances in Intelligent Data Analysis XIV, 2015.
DOI : 10.1007/978-3-319-24465-5_11
URL : https://hal.archives-ouvertes.fr/hal-01203561

L. Hubert and P. Arabie, Comparing partitions, Journal of classification, vol.2, issue.1, pp.193-218, 1985.
DOI : 10.1007/bf01908075

J. Jacques and C. Biernacki, Extension of model-based classification for binary data when training and test populations differ, Journal of Applied Statistics, vol.37, issue.5, pp.749-766, 2010.
DOI : 10.1080/02664760902889957
URL : https://hal.archives-ouvertes.fr/hal-00316080

M. Kharratzadeh, B. Renard, and M. Coates, Bayesian topic model approaches to online and time-dependent clustering, Digital Signal Processing, 2015.
DOI : 10.1016/j.dsp.2015.03.010

Y. Kim, J. Velcin, S. Bonnevay, and M. Rizoiu, Temporal Multinomial mixture for instance-oriented evolutionary clustering, Proc. of the European Conference on Information Retrieval, pp.593-604, 2015.
DOI : 10.1007/978-3-319-16354-3_66
URL : https://hal.archives-ouvertes.fr/hal-01134393

J. B. Kruskal and M. Wish, Multidimensional scaling, vol.11, 1978.

J. Lamirel, A new approach for automatizing the analysis of research topics dynamics: application to optoelectronics research, Scientometrics, vol.93, issue.1, pp.151-166, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00781576

G. J. Mclachlan and T. Krishnan, The EM algorithm and extensions, 2nd Edition. Wiley series in probability and statistics, 2008.

M. Meil?-a and D. Heckerman, An experimental comparison of model-based clustering methods, Machine Learning, vol.42, issue.1-2, pp.9-29, 2001.

V. Melnykov and R. Maitra, Finite mixture models and model-based clustering, Statistics Surveys, vol.4, pp.80-116, 2010.
DOI : 10.1214/09-ss053
URL : https://doi.org/10.1214/09-ss053

K. P. Murphy, Machine learning: a probabilistic perspective, 2012.

J. A. Nelder and R. Mead, A simplex method for function minimization, The computer journal, vol.7, issue.4, pp.308-313, 1965.
DOI : 10.1093/comjnl/7.4.308

M. D. Oliveira and J. Gama, MEC-monitoring clusters' transitions, STAIRS. pp, pp.212-224, 2010.
DOI : 10.1007/978-3-642-13062-5_12

S. Salvador and P. Chan, Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms, IEEE Conf. on Tools with Artificial Intelligence, pp.576-584, 2004.

G. Schwarz, Estimating the dimension of a model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.

C. Silvestre, M. G. Cardoso, and M. A. Figueiredo, Identifying the number of clusters in discrete mixture models, 2014.

M. Spiliopoulou, I. Ntoutsi, Y. Theodoridis, and R. Schult, MONIC: modeling and monitoring cluster transitions, Proc. of the ACM SIGKDD Int conf. on Knowledge discovery and data mining, pp.706-711, 2006.
DOI : 10.1007/978-3-642-40994-3_41
URL : https://link.springer.com/content/pdf/10.1007%2F978-3-642-40994-3_41.pdf

J. Velcin, Y. Kim, C. Brun, J. Dormagen, E. Sanjuan et al., Investigating the image of entities in social media: Dataset design and first results, Proc. of Language Resources and Evaluation Conference (LREC), 2014.
URL : https://hal.archives-ouvertes.fr/hal-02052420

K. S. Xu, M. Kliger, and A. O. Hero-iii, Adaptive evolutionary clustering, Data Mining and Knowledge Discovery, vol.28, issue.2, pp.304-336, 2014.
DOI : 10.1007/s10618-012-0302-x
URL : http://arxiv.org/pdf/1104.1990

T. Xu, Z. Zhang, P. S. Yu, and B. Long, Dirichlet process based evolutionary clustering, Data Mining, 2008. ICDM'08, pp.648-657, 2008.

T. Xu, Z. Zhang, P. S. Yu, and B. Long, Generative models for evolutionary clustering, ACM Trans. on Knowledge Discovery from Data, vol.6, issue.2, p.7, 2012.
DOI : 10.1145/2297456.2297459
URL : http://www.cs.binghamton.edu/~forweb/publications/npevo_final.pdf

J. Ypma, Introduction to nloptr: an r interface to nlopt, 2014.

S. Zhong and J. Ghosh, Generative model-based document clustering: a comparative study, Knowledge and Information Systems, vol.8, issue.3, pp.374-384, 2005.
DOI : 10.1007/s10115-004-0194-1