  Context. Machine-Learning (ML) solves problems by learning patterns from
data, with limited or no human guidance. In Astronomy, it is mainly applied to
large observational datasets, e.g. for morphological galaxy classification.
Aims. We apply ML to gravitational N-body simulations of star clusters that are
either formed by merging two progenitors or evolved in isolation, planning to
later identify Globular Clusters (GCs) that may have a history of merging from
observational data. Methods. We create mock-observations from simulated GCs,
from which we measure a set of parameters (also called features in the
machine-learning field). After dimensionality reduction on the feature space,
the resulting datapoints are fed to various classification algorithms. Using
repeated random subsampling validation we check whether the groups identified
by the algorithms correspond to the underlying physical distinction between
mergers and monolithically evolved simulations. Results. The three algorithms
we considered (C5.0 trees, k-nearest neighbour, and support-vector machines)
all achieve a test misclassification rate of about 10% without parameter
tuning, with support-vector machines slightly outperforming the others. The
first principal component of feature space correlates with cluster
concentration. If we exclude it from the regression, the performance of the
algorithms is only slightly reduced.
