  The colour bimodality of galaxies provides an empirical basis for theories of
galaxy evolution. However, the balance of processes that begets this bimodality
has not yet been constrained. A more detailed view of the galaxy population is
needed, which we achieve in this paper by using unsupervised machine learning
to combine multi-dimensional data at two different epochs. We aim to understand
the cosmic evolution of galaxy subpopulations by uncovering substructures
within the colour bimodality. We choose a clustering algorithm that models
clusters using only the most discriminative data available, and apply it to two
galaxy samples: one from the second edition of the GALEX-SDSS-WISE Legacy
Catalogue (GSWLC-2; $z \sim 0.06$), and the other from the VIMOS Public
Extragalactic Redshift Survey (VIPERS; $z \sim 0.65$). We cluster within a
nine-dimensional feature space defined purely by rest-frame
ultraviolet-through-near-infrared colours. Both samples are similarly
partitioned into seven clusters, breaking down into four of mostly star-forming
galaxies (including the vast majority of green valley galaxies) and three of
mostly passive galaxies. The separation between these two families of clusters
suggests differences in the evolution of their galaxies, and that these
differences are strongly expressed in their colours alone. The samples are
closely related, with star-forming/green-valley clusters at both epochs forming
morphological sequences, capturing the gradual internally-driven growth of
galaxy bulges. At high stellar masses, this growth is linked with quenching.
However, it is only in our low-redshift sample that additional, environmental
processes appear to be involved in the evolution of low-mass passive galaxies.
