  The large amount of data on galaxies, up to higher and higher redshifts, asks
for sophisticated statistical approaches to build adequate classifications.
Multivariate cluster analyses, that compare objects for their global
similarities, are still confidential in astrophysics, probably because their
results are somewhat difficult to interpret. We believe that the missing key is
the unavoidable characteristics in our Universe: evolution. Our approach, known
as Astrocladistics, is based on the evolutionary nature of both galaxies and
their properties. It gathers objects according to their "histories" and
establishes an evolutionary scenario among groups of objects. In this
presentation, I show two recent results on globular clusters and earlytype
galaxies to illustrate how the evolutionary concepts of Astrocladistics can
also be useful for multivariate analyses such as K-means Cluster Analysis.
