Clustering global entrepreneurship through data mining technique

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.