  Entrepreneurial regimes are topic, receiving ever more research attention.
Existing studies on entrepreneurial regimes mainly use common methods from
multivariate analysis and some type of institutional related analysis. In our
analysis, the entrepreneurial regimes is analyzed by applying a novel polygonal
symbolic data cluster analysis approach. Considering the diversity of data
structures in Symbolic Data Analysis (SDA), interval-valued data is the most
popular. Yet, this approach requires assuming equidistribution hypothesis. We
use a novel polygonal cluster analysis approach to address this limitation with
additional advantages: to store more information, to significantly reduce large
data sets preserving the classical variability through polygon radius, and to
open new possibilities in symbolic data analysis. We construct a dynamic
cluster analysis algorithm for this type of data with proving main theorems and
lemmata to justify its usage. In the empirical part we use dataset of Global
Entrepreneurship Monitor (GEM) for year 2015 to construct typologies of
countries based on responses to main entrepreneurial questions. The article
presents a novel approach to clustering in statistical theory (with novel type
of variables never accounted for) and application to a pressing issue in
entrepreneurship with novel results.
