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Advisor(s)
Abstract(s)
The purpose of this chapter is to contribute for the identification of groups of countries that share
similar patterns regarding the characteristics of Global Entrepreneurship and capturing features of
entrepreneurship by focusing on entrepreneurial attitudes and entrepreneurial activity. For this purpose,
67 countries from 2013 GEM survey were selected, and Data Mining Methodology was used. In
particular, evolutionary computation is used to determine a finite set of categories to describe the data
set according to multi-dimensional similarities among its objects. In other words, several clustering
algorithms where used, to get the best categories possible. The results show four clusters with different
entrepreneurial attitudes among the countries - very high, medium and low entrepreneurial attitudes
and entrepreneurial activities.
Description
Keywords
Entrepreneurship Data mining
Pedagogical Context
Citation
Fernandes, Paula O.; Lopes, Rui Pedro (2015). Clustering global entrepreneurship through data mining technique. In Farinha, Luís M. Carmo...[et al.] Handbook of Research on Global Competitive Advantage through Innovation and Entrepreneurship. IGI Global. p. 469-481. ISBN 978-1-4666-8348-8
Publisher
IGI Global