  Context. Galaxy evolution and the effect of environment are most often
studied using scaling relations or some regression analyses around some given
property. These approaches however do not take into account the complexity of
the physics of the galaxies and their diversification. Aims. We here
investigate the effect of cluster environment on the evolution of galaxies
through multivariate unsupervised classification and phylogenetic analyses
applied to two relatively large samples from the WINGS survey, one of cluster
members and one of field galaxies (2624 and 1476 objects respectively).
Methods. These samples are the largest ones ever analysed with a phylogenetic
approach in astrophysics. To be able to use the Maximum Parsimony (cladistics)
method, we first performed a pre-clustering in 300 clusters with a hierarchical
clustering technique, before applying it to these pre-clusters. All these
computations used seven parameters: B-V, log(Re), nV , $\mu$e , H$\beta$ ,
D4000 , log(M *). Results. We have obtained a tree for the combined samples and
do not find different evolutionary paths for cluster and field galaxies.
However, the cluster galaxies seem to have accelerated evolution in the sense
they are statistically more diversified from a primitive common ancestor. The
separate analyses show a hint for a slightly more regular evolution of the
variables for the cluster galaxies, which may indicate they are more
homogeneous as compared to field galaxies in the sense that the groups of the
latter appear to have more specific properties. On the tree for the cluster
galaxies, there is a separate branch which gathers rejunevated or stripped-off
groups of galaxies. This branch is clearly visible on the colour-magnitude
diagram, going back from the red sequence towards the blue one. On this
diagram, the distribution and the evolutionary paths of galaxies are strikingly
different for the two samples. Globally, we do not find any dominant variable
able to explain either the groups or the tree structures. Rather, co-evolution
appears everywhere, and could depend itself on environment or mass.
Conclusions. This study is another demonstration that unsupervised machine
learning is able to go beyond the simple scaling relations by taking into
account several properties together. The phylogenetic approach is invaluable to
trace the evolutionary scenarii and project them onto any biavariate diagram
without any a priori modelling. Our WINGS galaxies are all at low redshift, and
we now need to go to higher redshfits to find more primitve galaxies and
complete the map of the evolutionary paths of present day galaxies.
