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Entrepreneurial regimes are topic, receiving ever more research attention.Existing studies on entrepreneurial regimes mainly use common methods frommultivariate analysis and some type of institutional related analysis. In ouranalysis, the entrepreneurial regimes is analyzed by applying a novel polygonalsymbolic data cluster analysis approach. Considering the diversity of datastructures in Symbolic Data Analysis (SDA), interval-valued data is the mostpopular. Yet, this approach requires assuming equidistribution hypothesis. Weuse a novel polygonal cluster analysis approach to address this limitation withadditional advantages: to store more information, to significantly reduce largedata sets preserving the classical variability through polygon radius, and toopen new possibilities in symbolic data analysis. We construct a dynamiccluster analysis algorithm for this type of data with proving main theorems andlemmata to justify its usage. In the empirical part we use dataset of GlobalEntrepreneurship Monitor (GEM) for year 2015 to construct typologies ofcountries based on responses to main entrepreneurial questions. The articlepresents a novel approach to clustering in statistical theory (with novel typeof variables never accounted for) and application to a pressing issue inentrepreneurship with novel results.

New clustering approach for symbolic polygonal data: application to the clustering of entrepreneurial regimes