In the recent years, forests of decision trees have seen an increasing interest from the Machine Learning community since they allow to aggregate the decisions from a set of decision trees into one robust answer. However, this approach suffers from two well-known limits: first, their performances depend on the number of trees and thus finding the right size and how to aggregate decisions could be very difficult and second, large forests loose the interpretability capacity of a single decision tree. In this paper, we propose a new approach in which decisions trees from a forest are clustered to simplify the overall decision process while maintaining a large amount of decision trees and to facilitate the interpretation of the results. The preliminary results that are presented in this paper show the effectiveness of our approach.