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Clustering Global Entrepreneurship through Data Mining Technique

Clustering Global Entrepreneurship through Data Mining Technique
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Author(s): Paula Odete Fernandes (Polytechnic Institute of Bragança, Portugal) and Rui Pedro Lopes (Polytechnic Institute of Bragança, Portugal)
Copyright: 2015
Pages: 13
Source title: Handbook of Research on Global Competitive Advantage through Innovation and Entrepreneurship
Source Author(s)/Editor(s): Luís M. Carmo Farinha (Polytechnic Institute of Castelo Branco & NECE – Research Unit, Portugal), João J. M. Ferreira (University of Beira Interior & NECE – Research Unit, Portugal), Helen Lawton Smith (Birkbeck, University of London & Oxfordshire Economic Observatory, Oxford University, UK) and Sharmistha Bagchi-Sen (State University of New York – Buffalo, Buffalo, NY, USA)
DOI: 10.4018/978-1-4666-8348-8.ch027


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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.

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