C. Biernacki, G. Celeux, and E. 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

M. Ester, H. P. Kriegel, J. Sander, and X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, Proceedings of the 2nd International Conference on Knowledge Discovery and Data mining, pp.226-231, 1996.

J. Sander, M. Ester, H. P. Kriegel, and X. Xu, Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications, Data Mining and Knowledge Discovery, vol.2, issue.2, pp.169-194, 1998.
DOI : 10.1023/A:1009745219419

A. Moussa, A. Sbihi, and J. Postaire, A Markov random field model for mode detection in cluster analysis, Pattern Recognition Letters, vol.29, issue.9, pp.1197-1207, 2008.
DOI : 10.1016/j.patrec.2008.01.033

S. Ougiaroglou, Adaptive k-Nearest-Neighbor Classification Using a Dynamic Number of Nearest Neighbors, Advances in Databases and Information Systems, pp.66-82, 2007.
DOI : 10.1007/978-3-540-75185-4_7

P. Clifford, Markov Random Fields in statistics, Disorder in Physical Systems: A Volume in ~Honour of John M. Hammersley, pp.19-32, 1990.

R. S. Stoica, E. Gay, and A. Kretzschmar, Cluster Pattern Detection in Spatial Data Based on Monte Carlo Inference, Biometrical Journal, vol.115, issue.4, pp.505-519, 2007.
DOI : 10.1002/bimj.200610326

R. S. Stoica, V. J. Martinez, and E. Saar, Filaments in observed and mock galaxy catalogues, Astronomy and Astrophysics, vol.510, issue.A38, pp.1-12, 2010.
DOI : 10.1051/0004-6361/200912823

O. Alata, S. Burg, and A. Dupas, Grouping/degrouping point process, a point process driven by geometrical and topological properties of a partition in regions, Computer Vision and Image Understanding, vol.115, issue.9, pp.1324-1339, 2011.
DOI : 10.1016/j.cviu.2011.05.003

URL : https://hal.archives-ouvertes.fr/hal-00608143

Y. C. Chin and A. J. Baddeley, On connected component Markov point processes, Advances in Applied Probability, vol.31, issue.2, pp.279-282, 1999.
DOI : 10.1239/aap/1029955135

Y. C. Chin and A. J. Baddeley, Markov interacting component processes, Advances in Applied Probability, vol.39, issue.03, pp.597-619, 2000.
DOI : 10.1093/biomet/62.2.467

:. P. Green, Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika, vol.82, issue.4, pp.711-732, 1995.
DOI : 10.1093/biomet/82.4.711

J. G. Postaire and C. P. Vasseur, An Approximate Solution to Normal Mixture Identification with Application to Unsupervised Pattern Classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.3, issue.2, pp.163-179, 1981.
DOI : 10.1109/TPAMI.1981.4767074

X. T. Yuan, Agglomerative Mean-Shift Clustering, IEEE Transactions on Knowledge and Data Engineering, vol.24, issue.2, pp.209-219, 2012.
DOI : 10.1109/TKDE.2010.232

C. Arias, G. Chen, and G. Lerman, Spectral clustering based on local linear approximations, Electronic Journal of Statistics, vol.5, issue.0, pp.1537-1587, 2011.
DOI : 10.1214/11-EJS651

]. R. Arima-journal18, L. Giancarlo, G. Boscol, L. Pinello, and F. Utro, A methodology to assess the intrinsic discriminative ability of a distance function and its interplay with clustering algorithms for microarray data analysis, BMC Bioinformatics, vol.14, issue.1, p.6, 2013.