A. P. Dempster, L. N. , and D. B. Rubin, Maximum likelihood for incomplete data via the EM algorithm, J. Royal Stat. Soc. B, vol.39, issue.1, pp.1-38, 1977.

S. Wiseman, M. Blatt, and E. Domany, Superparamagnetic clustering of data, Physical Review E, vol.57, issue.4, pp.3767-3787, 1998.
DOI : 10.1103/PhysRevE.57.3767

J. Pearl, Probabilistic Reasoning in Intelligent Systems: Network of Plausible Inference, 1988.

J. S. Yedidia, W. T. Freeman, and Y. Weiss, Generalized belief propagation, Advances in Neural Information Processing Systems, pp.689-695, 2001.

F. R. Kschischang, B. J. Frey, and H. A. Loeliger, Factor graphs and the sum-product algorithm, IEEE Transactions on Information Theory, vol.47, issue.2, pp.498-519, 2001.
DOI : 10.1109/18.910572

B. Frey and D. Dueck, Clustering by Passing Messages Between Data Points, Science, vol.315, issue.5814, pp.972-976, 2007.
DOI : 10.1126/science.1136800

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.121.3145

M. Leone, M. Sumedha, and . Weigt, Clustering by soft-constraint affinity propagation: applications to gene-expression data, Bioinformatics, vol.23, issue.20, p.2708, 2007.
DOI : 10.1093/bioinformatics/btm414

M. Leone, M. Sumedha, and . Weigt, Unsupervised and semi-supervised clustering by message passing: Softconstraint affinity propagation, Eur. Phys. J. B, pp.125-135, 2008.

C. Fraley and A. Raftery, How many clusters?which clustering method? answer via model-based clustering. The Computer Journal [11] S. Still and W. Bialek. How many clusters?:an information-theoretic perspective, Neural Computation, vol.41, issue.16, pp.2483-2506, 1998.
DOI : 10.1093/comjnl/41.8.578

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.101.5035

S. Dudoit and J. Fridlyand, A prediction-based resampling method for estimating the number of clusters in a dataset, Genome Biology, vol.221, issue.7, pp.36-37, 2002.

K. Wang, J. Zhang, D. Li, X. Zhang, and T. Guo, Adaptive affinity propagation clustering, Acta Automatica Sinica, vol.33, issue.12, pp.1242-1246, 2007.

X. Zhang, C. Furtlehner, and M. Sebag, Data Streaming with Affinity Propagation, ECML/PKDD, pp.628-643, 2008.
DOI : 10.1007/978-3-540-87481-2_41

URL : https://hal.archives-ouvertes.fr/inria-00289679

X. Zhang, C. Furtlehner, J. Perez, C. Germain-renaud, and M. Sebag, Toward autonomic grids, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, pp.628-643, 2009.
DOI : 10.1145/1557019.1557126

URL : https://hal.archives-ouvertes.fr/inria-00393825

Y. Kabashima, Propagating Beliefs in Spin-Glass Models, Journal of the Physical Society of Japan, vol.72, issue.7, pp.1645-1649, 2003.
DOI : 10.1143/JPSJ.72.1645

S. Guha, A. Meyerson, N. Mishra, R. Motwani, and L. O. Callaghan, Clustering data streams: theory and practice, TKDE, pp.515-528, 2003.
DOI : 10.1109/TKDE.2003.1198387

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.108.9085

L. De-haan and A. Ferreira, Extreme Value Theory, Operations Research and Financial Engineering, 2006.
DOI : 10.1007/0-387-34471-3

L. Zelnik-manor and P. Perona, Self-tuning spectral clustering, Advances in Neural Information Processing Systems 17, pp.1601-1608, 2004.

M. Meila, The uniqueness of a good optimum for kmeans Fluctuations are neglected in this argument, ICML, pp.625-632, 2006.