  Machine learning can provide powerful tools to detect patterns in
multi-dimensional parameter space. We use K-means -a simple yet powerful
unsupervised clustering algorithm which picks out structure in unlabeled data-
to study a sample of quasar UV spectra from the Quasar Catalog of the 10th Data
Release of the Sloan Digital Sky Survey of Paris et al. (2014). Detecting
patterns in large datasets helps us gain insights into the physical conditions
and processes giving rise to the observed properties of quasars. We use K-means
to find clusters in the parameter space of the equivalent width (EW), the blue-
and red-half-width at half-maximum (HWHM) of the Mg II 2800 A line, the C IV
1549 A line, and the C III] 1908 A blend in samples of Broad Absorption-Line
(BAL) and non-BAL quasars at redshift 1.6-2.1. Using this method, we
successfully recover correlations well-known in the UV regime such as the
anti-correlation between the EW and blueshift of the C IV emission line and the
shape of the ionizing Spectra Energy distribution (SED) probed by the strength
of He II and the Si III]/C III] ratio. We find this to be particularly evident
when the properties of C III] are used to find the clusters, while those of Mg
II proved to be less strongly correlated with the properties of the other lines
in the spectra such as the width of C IV or the Si III]/C III] ratio. We
conclude that unsupervised clustering methods (such as K-means) are powerful
methods for finding "natural" binning boundaries in multidimensional datasets
and discuss caveats and future work.
