L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees, 1984.

Q. Quinlan, C. , and J. R. Quinlan, Improved use of continuous attributes in c4, Journal of Artificial Intelligence Research, vol.5, issue.4, pp.77-90, 1996.

F. Etat, Le numérique et les droits fondamentaux. Etudes et documents, Conseil d'Etat. La Documentation française, 2014.

M. Usama, K. B. Fayyad, and . Irani, On the handling of continuous-valued attributes in decision tree generation, Machine Learning, pp.87-102, 1992.

J. Gaudart, N. Graffeo, G. Barbet, S. Rebaudet, N. Dessay et al., SPODT: An R Package to Perform Spatial Partitioning, Journal of Statistical Software, issue.16, p.63, 2015.
DOI : 10.18637/jss.v063.i16

URL : https://hal.archives-ouvertes.fr/hal-01208245

J. Kmenta, Elements of Econometrics, pp.298-334
DOI : 10.3998/mpub.15701

K. Koperski, J. Hah, and N. Stefanovic, An efficient two-step method for classification of spatial data, Symposium on Spatial Data Handling (SDH '98), pp.45-54, 1998.

J. and R. Quinlan, C4.5: Programs for Machine Learning, 1993.

J. Rousu and T. Elomaa, Efficient multisplitting revisited: Optima-preserving elimination of partition candidates, Data Mining and Knowledge Discovery, vol.8, issue.2, pp.97-126, 2004.

Y. Suzuki-einoshin-yokoi-hideto-takabayashi-katsuhiko-yamada, Decision-tree induction from Figure 5: Comparison between periodic and classical splits time-series data based on a standard-example split test, Twentieth International Conference on Machine Learning (ICML), pp.840-847, 2003.