  Based on the collected multiwavelength data, namely in the radio (NVSS,
FIRST, RATAN-600), IR (WISE), optical (Pan-STARRS), UV (GALEX), and X-ray
(ROSAT, Swift-XRT) ranges, we have performed a cluster analysis for the blazars
of the Roma-BZCAT catalog. Using two machine learning methods, namely a
combination of PCA with k-means clustering and Kohonen's self-organizing maps,
we have constructed an independent classification of the blazars (five classes)
and compared the classes with the known Roma-BZCAT classification (FSRQs, BL
Lacs, galaxy-dominated BL Lacs, and blazars of an uncertain type) as well as
with the high synchrotron peaked blazars (HSP) from the 3HSP catalog and
blazars from the TeVCat catalog. The obtained groups demonstrate concordance
with the BL Lac/FSRQ classification along with a continuous character of the
change in the properties. The group of HSP blazars stands out against the
overall distribution. We examine the characteristics of the five groups and
demonstrate distinctions in their spectral energy distribution shapes. The
effectiveness of the clustering technique for objective analysis of
multiparametric arrays of experimental data is demonstrated.
