M. Belkin and P. Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, Adv Neural Inf Process Syst, vol.14, pp.585-591, 2001.

R. D. Bock, Estimating item parameters and latent ability when responses are scored in two or more nominal categories, Psychometrika, vol.37, pp.29-51, 1972.

L. Breiman, Heuristics of instability and stabilization in model selection, Ann Stat, vol.24, p.6, 1996.

L. Breiman, Random forests, Mach Learn, vol.45, issue.1, pp.5-32, 2001.

L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and regression trees, 1984.

X. Chen, X. Xu, J. Z. Huang, and Y. Ye, Tw-k-means: automated two-level variable weighting clustering algorithm for multiview data, IEEE Trans Knowl Data Eng, vol.25, issue.4, pp.932-944, 2013.

R. Fisher, The use of multiple measurements in taxonomic problems, Ann Eugen, vol.7, pp.179-188, 1936.

R. Fraiman, B. Ghattas, and M. Svarc, Interpretable clustering using unsupervised binary trees, Adv Data Anal Classif, vol.7, pp.125-145, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01297499

B. Ghattas, Importance des variables dans les méthodes cart, Modulad, vol.24, pp.29-39, 1999.

B. Ghattas, P. Michel, and L. Boyer, Clustering nominal data using unsupervised binary decision trees: comparisons with the state of the art methods, Pattern Recognit, vol.67, pp.177-185, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01620109

I. Guyon, J. Weston, S. Barnhill, and V. N. Vapnik, Gene selection for cancer classification using support vector machines, Mach Learn, vol.46, issue.1-3, pp.389-422, 2002.

A. Liaw and M. Wiener, Classification and regression by randomforest, R News, vol.2, issue.3, pp.12-22, 2002.

H. Liu and L. Yu, Toward integrating feature selection algorithms for classifcation and clustering, IEEE TKDE, vol.17, pp.491-502, 2005.

J. Macqueen, Some methods for classification and analysis of multivariate observations, Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, pp.281-297, 1967.

E. Muraki, A generalized partial credit model: application of an em algorithm, Appl Psychol Measur, vol.16, pp.159-176, 1992.

. R-core-team, R: a language and environment for statistical computing. R Foundation for Statistical Computing, 2013.

A. Rakotomamonjy, Variable selection using SVM-based criteria, J Mach Learn Res, vol.3, pp.1357-1370, 2003.

M. Reif, mcIRT: IRT models for multiple choice items, 2014.

D. Rizopoulos, ltm: an R package for latent variable modelling and item response theory analyses, J Stat Softw, vol.17, issue.5, pp.1-25, 2006.

J. Weston, A. Elisseff, B. Schoelkopf, and M. Tipping, Use of the zero norm with linear models and kernel methods, J Mach Learn Res, vol.3, pp.1439-1461, 2003.

G. Williams, J. Z. Huang, X. Chen, Q. Wang, and L. Xiao, wskm: weighted k-means clustering, 2015.

L. Zhu, L. Miao, and D. Zhang, Iterative Laplacian score for feature selection, Pattern Recognit, vol.321, pp.80-87, 2012.

, Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations