We herein introduce a new method of interpretable clustering that uses unsu-
pervised binary trees. It is a three-stage procedure, the rst stage of which entails
a series of recursive binary splits to reduce the heterogeneity of the data within
the new subsamples. During the second stage (pruning), consideration is given to
whether adjacent nodes can be aggregated. Finally, during the third stage (join-
ing), similar clusters are joined together, even if they do not share the same parent
originally. Consistency results are obtained, and the procedure is used on simulated
and real data sets.
