@misc{Pełka_Marcin_Analysis_2018,
 author={Pełka, Marcin},
 identifier={DOI: 10.15611/eada.2018.3.06},
 year={2018},
 rights={Pewne prawa zastrzeżone na rzecz Autorów i Wydawcy},
 publisher={Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu},
 description={Econometrics = Ekonometria, 2018, Vol. 22, No. 3, s. 84-98},
 language={eng},
 abstract={Innovations play a very important role in the modern economy. They are the key to a higher quality of life, better jobs and economy and sustainable development. The innovation policy is a key element of both national and European Union strategy. The main aim of this paper is to present an ensemble clustering of European Union countries (member states) considering their innovativeness. In the empirical section, symbolic density-based ensemble clustering is used to obtain the co-occurrence matrix. The paper uses symbolicDA, clusterSim and dbscan packages of R software for all calculations. Four different clusters where obtained in the result of clustering. Cluster 1 contains highinnovative countries (innovation leaders). This cluster is also the least homogenous. Cluster 2 contains post-communist countries mainly from central Europe. These countries can be seen as rather mid-low innovative (they try to “catch up” with innovation leaders). Cluster 3 contains moderate innovators. Cluster 4 contains two countries that are also mid-innovative},
 title={Analysis of innovations in the European Union via ensemble symbolic density clustering},
 type={artykuł},
 keywords={innovations, European Union, symbolic data analysis, ensemble clustering, innowacje, Unia Europejska, analiza danych symbolicznych, klasyfikacja wielomodelowa},
}