  Context. Discovery of new variability classes in large surveys using
multivariate statistics techniques such as clustering, relies heavily on the
correct understanding of the distribution of known classes as point processes
in parameter space. Aims. Our objective is to analyze the correspondence
between the classical stellar variability types and the clusters found in the
distribution of light curve parameters and colour indices of stars in the CoRoT
exoplanet sample. The final aim is to help in the identification on new types
of variability by first identifying the well known variables in the CoRoT
sample. Methods. We apply unsupervised classification algorithms to identify
clusters of variable stars from modes of the probability density distribution.
We use reference variability databases (Hipparcos and OGLE) as a framework to
calibrate the clustering methodology. Furthermore, we use the results from
supervised classification methods to interpret the resulting clusters.
Results.We interpret the clusters in the Hipparcos and OGLE LMC databases in
terms of large-amplitude radial pulsators in the classical instability strip
and of various types of eclipsing binaries. The Hipparcos data also provide
clear distributions for low-amplitude nonradial pulsators. We show that the
preselection of targets for the CoRoT exoplanet programme results in a
completely different probability density landscape than the OGLE data, the
interpretation of which involves mainly classes of low-amplitude variability in
main-sequence stars. Our findings will be incorporated to improve the
supervised classification used in the CoRoT catalogue production, once the
existence of new classes or subtypes will be confirmed from complementary
spectroscopic observations.
