Małgorzata Markowska , Bartłomiej Jefmański

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“Europe 2020 Strategy” presents the vision of European economy development, in which smart development, i.e. development based on knowledge and innovation, constitutes one of major priorities. Smart specialization which refers to enterprises, research centres and high schools cooperating in defining the most promising areas of specialization in a given region, represents one of crucial smart development components. Smart specialization refers to both, the concept and the tool, allowing regions and countries to assess their unique position in knowledge-based economy. This knowledge should not be underestimated at the stage of preparing regional and interregional policy assumptions and specifying directions for the distribution of financial means allocated to further development of regions, constructing their advantage in regional space and position in knowledge based economy. Therefore, the essential objective of the hereby study is to distinguish classes of regions in European space with regard to one complex phenomenon, i.e. smart specialization. For this reason both classical and fuzzy classification methods were applied. Such approach facilitated e.g. specifying these regions for which it is difficult to provide clear division regarding their membership in distinguished classes. They are the regions which “keep searching” for their optimum path of smart development and which should be offered particular attention by entities managing development at regional, national and overall EU level.


fuzzy region classification, fuzzy c-means, Europe 2020, smart growth of regions


[1] A Digital Agenda for Europe, Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions, EUROPEAN COMMISSION, COM(2010) 245 final/2, Brussels, 2010.

[2] Bezdek J.C., [1981], Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York.

[3] Bock H.-H., [2008], Origins and extensions of the k-means algorithm in cluster analysis, “Electronic Journal for History of Probability and Statistics”, vol. 4, no. 2.

[4] Cox E., [2005], Fuzzy modeling and genetic algorithms for data mining and exploration, Morgan Kaufmann Publishers, San Francisco.

[5] Digital Agenda Scoreboard, Commission Staff Working Paper, SEC(2011) 708, European Commission, Brussels, 2011.

[6] Domanska W., [2010], Strategia rozwoju Europy do 2020 r., „Wiadomosci Statystyczne” no. 8 (591).

[7] Dunn A., [1973], Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well- Separated Clusters, “Journal of Cybernetics”, vol. 3.

[8] EUROPA 2020. Strategia na rzecz inteligentnego i zrównowazonego rozwoju sprzyjajacego właczeniu społecznemu. Komisja Europejska, Komunikat Komisji, KOM(2010) 2020 wersja ostateczna, Bruksela 2010.

[9] Giannitsis A., Kager M., [2009], Technology and specialisation: Dilemmas, options and risks?, Expert group “Knowledge for Growth”.

[10] H¨oppner F., [1999], Fuzzy cluster analysis: methods for classification, data analysis, and image recognition, John Wiley&Sons, Chichester.

[11] Innovation Union Competitiveness report, Directorate-General for Research and Innovation, Directorate-General for Research and Innovation, Research and Innovation, European Commission, Luxembourg: Publications Office of the European Union, 2011.

[12] Jajuga K., [1990], Statystyczna teoria rozpoznawania obrazów, PWN, Warszawa.

[13] Lasek M., [2002], Data Mining. Zastosowania w analizach i ocenach klientów bankowych. Oficyna Wydawnicza „Zarzadzanie i finanse”, Biblioteka Menadzera i Bankowca, Warszawa.

[14] Leung Y., [1983], Fuzzy Sets Approach to Spatial Analysis and Planning, a Nontechnical Evaluation, “Geografiska Annaler. Series B”, vol. 65, no. 2.

[15] Nascimento S., Mirkin B., Moura-Pires F., [2000], A fuzzy clustering model of data and fuzzy cmeans. IEEE International Conference on Fuzzy Systems: Soft Computing in the Information Age, vol. 1.

[16] Pawełek B., [2008], Metody normalizacji zmiennych w badaniach porównawczych złozonych zjawisk ekonomicznych, Wydawnictwo UE w Krakowie, Kraków.

[17] Polityka regionalna jako czynnik przyczyniajacy sie do inteligentnego rozwoju w ramach strategii Europa 2020, Komunikat Komisji do Parlamentu Europejskiego, Rady, Europejskiego Komitetu Ekonomiczno-Społecznego i Komitetu Regionów, KOM(2010) 553, Bruksela, 2010.

[18] Regions in the European Union. Nomenclature of territorial units for statistics NUTS 2006/EU-27, Series: Methodologies and Working Papers, European Commission, Luxemburg 2007.

[19] Sokołowski A., [1992], Empiryczne testy istotnosci w taksonomii, Wydawnictwo Akademii Ekonomicznej w Krakowie, Kraków.

[20] Steinley D., [2006], K-means clustering: a half century synthesis, „British Journal on Mathematical and Statistical Psychology”, vol. 59.

[21] Strategia na rzecz inteligentnego i zrównowazonego rozwoju sprzyjajacego właczeniu społecznemu, Krajowy program reform Europa 2020, Ministerstwo Gospodarki, Warszawa 2010.

[22] Walesiak M., Gatnar E. (red.), [2009], Statystyczna analiza danych z wykorzystaniem programu R, Wydawnictwo Naukowe PWN, Warszawa.

[23] Wintjes R., Hollanders H., [2010], The regional impact of technological change in 2020 – Synthesis report, European Commission, DG Regional Policy, Brussels.

[24] Wysocki F., [2010], Metody taksonomiczne w rozpoznawaniu typów ekonomicznych rolnictwa i obszarów wiejskich, Wydawnictwo Uniwersytetu Przyrodniczego w Poznaniu, Poznan.

[25] Youth on the Move, Publications Office of the European Union, European Union, Luxembourg, 2010.

[26] Zimmermann J.H., [2001], Fuzzy set theory and its applications. Fourth edition, Kluwer Academic Publishers, Boston.

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