  We present an information-theoretic approach inspired by distributional
clustering to assess the structural heterogeneity of particulate systems. Our
method identifies communities of particles that share a similar local structure
by harvesting the information hidden in the spatial variation of two- or
three-body static correlations. This corresponds to an unsupervised machine
learning approach that infers communities solely from the particle positions
and their species. We apply this method to three models of supercooled liquids
and find that it detects subtle forms of local order, as demonstrated by a
comparison with the statistics of Voronoi cells. Finally, we analyze the
time-dependent correlation between structural communities and particle mobility
and show that our method captures relevant information about glassy dynamics.
