  In this paper it is demonstrated that the application of principal components
analysis for regional cluster modelling and analysis is essential in the
situations where there is significant multicollinearity among several
parameters, especially when the dimensionality of regional data is measured in
tens. The proposed principal components model allows for same-quality
representation of the clustering of regions. In fact, the clusters become more
distinctive and the apparent outliers become either more pronounced with the
component model clustering or are alleviated with the respective hierarchical
cluster. Thus, a five-component model was obtained and validated upon 85
regions of Russian Federation and 19 socio-economic parameters. The principal
components allowed to describe approximately 75 percent of the initial
parameters variation and enable further simulations upon the studied variables.
The cluster analysis upon the principal components modelling enabled better
exposure of regional structure and disparity in economic development in Russian
Federation, consisting of four main clusters: the few-numbered highest
development regions, the clusters with mid-to-high and low economic
development, and the "poorest" regions. It is observable that the development
in most regions relies upon resource economy, and the industrial potential as
well as inter-regional infrastructural potential are not realized to their
fullest, while only the wealthiest regions show highly developed economy, while
the industry in other regions shows signs of stagnation which is scaled further
due to the conditions entailed by economic sanctions and the recent Covid-19
pandemic. Most Russian regions are in need of additional public support and
industrial development, as their capital assets potential is hampered and,
while having sufficient labor resources, their donorship will increase.
