  Most stars form in clumpy and sub-structured clusters. These properties also
emerge in hydro-dynamical simulations of star-forming clouds, which provide a
way to generate realistic initial conditions for $N-$body runs of young stellar
clusters. However, producing large sets of initial conditions by
hydro-dynamical simulations is prohibitively expensive in terms of
computational time. We introduce a novel technique for generating new initial
conditions from a given sample of hydro-dynamical simulations, at a tiny
computational cost. In particular, we apply a hierarchical clustering algorithm
to learn a tree representation of the spatial and kinematic relations between
stars, where the leaves represent the single stars and the nodes describe the
structure of the cluster at larger and larger scales. This procedure can be
used as a basis for the random generation of new sets of stars, by simply
modifying the global structure of the stellar cluster, while leaving the
small-scale properties unaltered.
