  While we already seem to have a general scenario of the evolution of
different types of galaxies, a complete and satisfactory understanding of the
processes that led to the formation of all the variety of today's galaxy types
is still beyond our reach. To solve this problem, we need both large datasets
reaching high redshifts and novel methodologies for dealing with them. The
VIPERS survey statistical power, which observed $\sim90,000$ galaxies at $z >
0.5$, and the application of an unsupervised clustering algorithm allowed us to
distinguish 12 galaxy classes. Studies of their environmental dependence
indicate that this classification may actually reflect different galaxy
evolutionary paths. For instance, a class of the most passive red galaxies
gathers galaxies $\sim20\%$ smaller than other red galaxies of a similar
stellar mass, revealing the first sample of red nuggets at intermediate
redshift. On the other end, a class of blue dwarf galaxies is composed mainly
of AGN, challenging commonly used mid-infrared AGN selections.
