Formal Concept Analysis (FCA) and concept lattices have shown their e ffectiveness for binary clustering and concept learning. Moreover, several links between FCA and unsupervised data mining tasks such as itemset mining and association rules extraction have been emphasized. Several works also studied FCA in a supervised framework, showing that popular machine learning tools such as decision trees can be extracted from concept lattices. In this paper, we investigate the links between FCA and decision trees with numerical data. Recent works showed the effciency of "pattern structures" to handle numerical data in FCA, compared to traditional discretization methods such as conceptual scaling.