M. M. Deza and E. Deza, Encyclopedia of distances, Encyclopedia of Distances, pp.1-583, 2009.
DOI : 10.1007/978-3-642-00234-2_1

T. Shi and S. Horvath, Unsupervised Learning With Random Forest Predictors, Journal of Computational and Graphical Statistics, vol.15, issue.1, pp.118-138, 2006.
DOI : 10.1198/106186006X94072

URL : http://labs.genetics.ucla.edu/horvath/RFclustering/RFclustering/RandomForestHorvath.pdf

L. Breiman, Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.
DOI : 10.1023/A:1010933404324

B. Percha, Y. Garten, and R. B. Altman, Discovery and explanation of drugdrug interactions via text mining, Pacific Symposium on Biocomputing, pp.410-421, 2012.

J. Friedman, T. Hastie, and R. Tibshirani, The elements of statistical learning, series in statistics, 2001.

H. L. Kim, D. Seligson, X. Liu, N. Janzen, M. H. Bui et al., USING TUMOR MARKERS TO PREDICT THE SURVIVAL OF PATIENTS WITH METASTATIC RENAL CELL CARCINOMA, The Journal of Urology, vol.173, issue.5, pp.1496-1501, 2005.
DOI : 10.1097/01.ju.0000154351.37249.f0

M. C. Abba, H. Sun, K. A. Hawkins, J. A. Drake, Y. Hu et al., Breast Cancer Molecular Signatures as Determined by SAGE: Correlation with Lymph Node Status, Molecular Cancer Research, vol.5, issue.9, pp.881-890, 2007.
DOI : 10.1158/1541-7786.MCR-07-0055

URL : http://mcr.aacrjournals.org/content/molcanres/5/9/881.full.pdf

S. I. Rennard, N. Locantore, B. Delafont, R. Tal-singer, E. K. Silverman et al., Identification of Five Chronic Obstructive Pulmonary Disease Subgroups with Different Prognoses in the ECLIPSE Cohort Using Cluster Analysis, Annals of the American Thoracic Society, vol.152, issue.3, pp.303-312, 2015.
DOI : 10.1164/rccm.200709-1356OC

K. Y. Peerbhay, O. Mutanga, and R. Ismail, Random Forests Unsupervised Classification: The Detection and Mapping of <italic>Solanum mauritianum</italic> Infestations in Plantation Forestry Using Hyperspectral Data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.8, issue.6, pp.3107-3122, 2015.
DOI : 10.1109/JSTARS.2015.2396577

P. Geurts, D. Ernst, and L. Wehenkel, Extremely randomized trees, Machine Learning, vol.63, issue.1, pp.3-42, 2006.
DOI : 10.1007/s10994-006-6226-1

URL : https://hal.archives-ouvertes.fr/hal-00341932

W. M. Rand, Objective Criteria for the Evaluation of Clustering Methods, Journal of the American Statistical Association, vol.15, issue.336, pp.846-850, 1971.
DOI : 10.1080/01621459.1963.10500845

L. Hubert and P. Arabie, Comparing partitions, Journal of Classification, vol.78, issue.1, pp.193-218, 1985.
DOI : 10.1007/978-3-642-69024-2_27

R. A. Fisher and M. Marshall, Iris data set, 1936.

M. Forina, An extendible package for data exploration, classification and correlation. Institute of Pharmaceutical and Food Analysis and Technologies, p.16147, 1991.

O. L. Mangasarian and W. H. Wolberg, Cancer diagnosis via linear programming, 1990.
DOI : 10.1287/opre.43.4.570

W. H. Kruskal and W. A. Wallis, Use of ranks in one-criterion variance analysis, Journal of the American statistical Association, issue.260, pp.47583-621, 1952.

A. Strehl and J. Ghosh, Cluster ensembles-a knowledge reuse framework for combining multiple partitions, Journal of machine learning research, vol.3, pp.583-617, 2002.

H. Elghazel and A. Aussem, Feature Selection for Unsupervised Learning Using Random Cluster Ensembles, 2010 IEEE International Conference on Data Mining, pp.168-175, 2010.
DOI : 10.1109/ICDM.2010.137

F. Pedregosa, Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905