  We present an application of unsupervised Machine Learning Clustering to the
PAU Survey of galaxy spectral energy distribution (SED) within the COSMOS
field. The clustering algorithm is implemented and optimized to get the
relevant groups in the data SEDs. We find 12 groups from a total number of
5,234 targets in the survey at $0.01 <$ z $< 0.28$. Among the groups, 3,545
galaxies (68\%) show emission lines in the SEDs. These groups also include
1,689 old galaxies with no active star formation. We have fitted the SED to
every single galaxy in each group with CIGALE. The mass, age and specific star
formation rates (sSFR) of the galaxies range from $0.15 <$ age/Gyr $< 11$; $6
<$ log (M$_{\star}$/M$_{\odot}$) $< 11.26$, and $-14.67 <$ log (sSFR/yr
$^{-1}$) $< -8$. The groups are well defined in their properties with galaxies
having clear emission lines also having lower mass, are younger and have higher
sSFR than those with elliptical like patterns. The characteristic values of
galaxies showing clear emission lines are in agreement with the literature for
starburst galaxies in COSMOS and GOODS-N fields at low redshift. The
star-forming main sequence, sSFR vs. stellar mass and UVJ diagram show clearly
that different groups fall into different regions with some overlap among
groups. Our main result is that the joint of low-resolution (R $\sim$ 50)
photometric spectra provided by the PAU survey together with the unsupervised
classification provides an excellent way to classify galaxies. Moreover, it
helps to find and extend the analysis of extreme ELGs to lower masses and lower
SFRs in the local Universe.
