  Subjective classification of spiral galaxies is not sufficient for studying
the effect of bars on their physical characteristics. In reality the problem is
to comprehend the complex correlations in a multivariate parametric space.
Multivariate tools are the best ones for understanding this complex
correlation. In this work an objective classification of a large set (26,089)
of spiral galaxies was compiled as a value added galaxy catalogue from sdss DR
15 virtual data archive.
  Initially for dimensionality reduction, Independent Component Analysis is
performed to determine a set of Independent Components that are linear
combinations of 48 observed features (namely ionised lines, Lick indices,
photometric and morphological properties). Subsequently a K-means cluster
analysis is carried out on the basis of the 14 best chosen Independent
Components to obtain 12 distinct homogeneous groups of spiral galaxies. Amongst
these, 3 groups are the oldest ones (1.6 Gyr - 5.9 Gyr), while 5 groups fall in
the medium aged category (1.4 Gyr - 1.6 Gyr), 2 groups consist of only unbarred
spirals, 1 group is the youngest one and the remaining one is an outlier. In
many groups there are clear indication of recurrent bar formation phenomena
which is consistent with few previous simulation works. In order to study the
robustness a second method of clustering by Gaussian Mixture Modeling Method
(GMMBC) is applied.
