  The morphological classification of galaxies is considered a relevant issue
and can be approached from different points of view. The increasing growth in
the size and accuracy of astronomical data sets brings with it the need for the
use of automatic methods to perform these classifications. The aim of this work
is to propose and evaluate a method for automatic unsupervised classification
of kinematic morphologies of galaxies that yields a meaningful clustering and
captures the variations of the fundamental properties of galaxies. We obtain
kinematic maps for a sample of 2064 galaxies from the largest simulation of the
EAGLE project that mimics integral field spectroscopy (IFS) images. These maps
are the input of a dimensionality reduction algorithm followed by a clustering
algorithm. We analyse the variation of physical and observational parameters
among the clusters obtained from the application of this procedure to different
inputs. The inputs studied in this paper are (a) line-of-sight velocity maps
for the whole sample of galaxies observed at fixed inclinations, (b)
line-of-sight velocity, dispersion, and flux maps together for the whole sample
of galaxies observed at fixed inclinations, (c) line-of-sight velocity,
dispersion, and flux maps together for two separate subsamples of edge-on
galaxies with similar amount of rotation, and (d) line-of-sight velocity,
dispersion, and flux maps together for galaxies from different observation
angles mixed. The application of the method to solely line-of-sight velocity
maps achieves a clear division between slow rotators (SRs) and fast rotators
(FRs) and can differentiate rotation orientation. By adding the dispersion and
flux information at the input, low rotation edge-on galaxies are separated
according to their shapes. Abridged.
