  Cluster algebras have recently become an important player in mathematics and
physics. In this work, we investigate them through the lens of modern data
science, specifically with techniques from network science and machine
learning. Network analysis methods are applied to the exchange graphs for
cluster algebras of varying mutation types. The analysis indicates that when
the graphs are represented without identifying by permutation equivalence
between clusters an elegant symmetry emerges in the quiver exchange graph
embedding. The ratio between number of seeds and number of quivers associated
to this symmetry is computed for finite Dynkin type algebras up to rank 5, and
conjectured for higher ranks. Simple machine learning techniques successfully
learn to classify cluster algebras using the data of seeds. The learning
performance exceeds 0.9 accuracies between algebras of the same mutation type
and between types, as well as relative to artificially generated data.
