Abstract:

In the context of an increased global competition, harnessing scientific and technological potential of each member country is one of the major objectives of the EU.  A pragmatic approach to this goal involves development and implementation of a unitary policy for all member countries that are similar with respect to a series of indicators.  Identifying groups of similar countries in terms of indicators that reflect the percentage of employees in the technology and knowledge-intensive sectors represents the objective of this paper. Implicitely, the resulted groups provide a classification of the EU countries. For this purpose, in this paper, we propose a clustering analysis using DBSACN algorithm (Density Based Spatial Clustering of Applications with Noise) implemented in R language. As a result of this analysis, two clusters have been identified, validated and characterized and three oultiers countries (noises) have been detected.