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Analysis of Innovations in the European Union Via Ensemble Symbolic Density Clustering

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  • Pełka Marcin

    (Wrocław University of Economics,Wrocław,Poland)

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.

Suggested Citation

  • Pełka Marcin, 2018. "Analysis of Innovations in the European Union Via Ensemble Symbolic Density Clustering," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 22(3), pages 84-98, September.
    • Handle: RePEc:vrs:eaiada:v:22:y:2018:i:3:p:84-98:n:6
    DOI: 10.15611/eada.2018.3.06
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    References listed on IDEAS

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    1. Małgorzata Stec, 2009. "Innowacyjność krajów Unii Europejskiej," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 11-12, pages 45-65.
    2. Dragana Radicic & Geoffrey Pugh & Hugo Hollanders & René Wintjes & Jon Fairburn, 2016. "The impact of innovation support programs on small and medium enterprises innovation in traditional manufacturing industries: An evaluation for seven European Union regions," Environment and Planning C, , vol. 34(8), pages 1425-1452, December.
    3. Hornik, Kurt, 2005. "A CLUE for CLUster Ensembles," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i12).
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    Cited by:

    1. Joanna Błach & Monika Wieczorek-Kosmala & Joanna Trzęsiok, 2020. "Innovation in SMEs and Financing Mix," JRFM, MDPI, vol. 13(9), pages 1-19, September.

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