A. Banerjee, I. S. Dhillon, J. Ghosh, and S. Sra, Clustering on the unit hypersphere using von Mises-Fisher distributions, In: Journal of Machine Learning Research, pp.1345-1382, 2005.

C. Biernacki, G. Celeux, and G. Govaert, Assessing a mixture model for clustering with the integrated completed likelihood, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.7, pp.719-725, 2000.
DOI : 10.1109/34.865189

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.3-4, pp.561-575, 2003.
DOI : 10.1016/S0167-9473(02)00163-9

M. A. Figueiredo and A. K. Jain, Unsupervised learning of finite mixture models, IEEE Transactions on Pattern Analysis and Machine Intelligence, 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

C. Fraley and A. E. Raftery, Model-based methods of classification: using the mclust software in chemometrics, Journal of Statistical Software, vol.18, issue.6, pp.1-13, 2007.

V. Garcia and F. Nielsen, Simplification and hierarchical representations of mixtures of exponential families, Signal Processing, vol.90, issue.12, pp.3197-3212, 2010.
DOI : 10.1016/j.sigpro.2010.05.024

M. A. Hasnat, O. Alata, and A. Trémeau, Unsupervised Clustering of Depth Images Using Watson Mixture Model, 2014 22nd International Conference on Pattern Recognition, pp.214-219, 2014.
DOI : 10.1109/ICPR.2014.46

URL : https://hal.archives-ouvertes.fr/ujm-01005179

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.4, issue.2, 2015.
DOI : 10.1007/s11222-015-9576-3

URL : https://hal.archives-ouvertes.fr/hal-01220515

L. Hubert and P. Arabie, Comparing partitions, Journal of Classification, vol.78, issue.1, pp.193-218, 1985.
DOI : 10.1007/BF01908075

R. Maitra, Initializing Partition-Optimization Algorithms, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.6, issue.1, pp.144-157, 2009.
DOI : 10.1109/TCBB.2007.70244

G. J. Mclachlan and T. Krishnan, The EM algorithm and extensions. 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.
DOI : 10.1023/A:1007648401407

V. Melnykov and R. Maitra, Finite mixture models and model-based clustering, Statistics Surveys, vol.4, issue.0, pp.80-116, 2010.
DOI : 10.1214/09-SS053

S. Salvador and P. Chan, Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms, 16th IEEE International Conference on Tools with Artificial Intelligence, pp.576-584, 2004.
DOI : 10.1109/ICTAI.2004.50

C. Silvestre, M. G. Cardoso, and M. A. Figueiredo, Identifying the number of clusters in discrete mixture models. arXiv preprint arXiv:1409, p.7419, 2014.

S. Vaithyanathan and B. Dom, Model-based hierarchical clustering, Proc of the Uncertainty in Artificial Intelligence, pp.599-608, 2000.

S. Zhong and J. Ghosh, A unified framework for model-based clustering, Journal of Machine Learning Research, vol.4, pp.1001-1037, 2003.

S. Zhong and J. Ghosh, Generative model-based document clustering: a comparative study, Knowledge and Information Systems, vol.4, issue.3, pp.374-384, 2005.
DOI : 10.1007/s10115-004-0194-1