IDEAS home Printed from https://ideas.repec.org/a/vrs/eaiada/v22y2018i3p84-98n6.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.15611/eada.2018.3.06
    Download Restriction: no
    File URL: https://libkey.io/10.15611/eada.2018.3.06?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Małgorzata Stec, 2009. "Innowacyjność krajów Unii Europejskiej," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 11-12, pages 45-65.
    3. Hornik, Kurt, 2005. "A CLUE for CLUster Ensembles," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i12).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stojčić, Nebojša, 2021. "Social and private outcomes of green innovation incentives in European advancing economies," Technovation, Elsevier, vol. 104(C).
    2. Marcin Pełka, 2012. "Ensemble approach for clustering of interval-valued symbolic data," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 13(2), pages 335-342, June.
    3. Wu, Han-Ming, 2011. "On biological validity indices for soft clustering algorithms for gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1969-1979, May.
    4. Hornik, Kurt & Grün, Bettina, 2014. "movMF: An R Package for Fitting Mixtures of von Mises-Fisher Distributions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i10).
    5. Luis Lorenzo & Javier Arroyo, 2022. "Analysis of the cryptocurrency market using different prototype-based clustering techniques," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-46, December.
    6. Boztug, Yasemin & Reutterer, Thomas, 2008. "A combined approach for segment-specific market basket analysis," European Journal of Operational Research, Elsevier, vol. 187(1), pages 294-312, May.
    7. Hornik, Kurt & Feinerer, Ingo & Kober, Martin & Buchta, Christian, 2012. "Spherical k-Means Clustering," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i10).
    8. Adel Ben Khalifa, 2018. "Government Supports and Technology Adoption: Evidence from Tunisia," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 43(4), pages 41-70, December.
    9. Fionn Murtagh, 2009. "The Remarkable Simplicity of Very High Dimensional Data: Application of Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 26(3), pages 249-277, December.
    10. Antonio Prencipe & Luciano D'Amico & Danilo Boffa & Christian Corsi, 2024. "The effect of output additionality of public funding support on firm innovation. Evidence from firms of different sizes," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2278-2299, April.
    11. Brock, Guy & Pihur, Vasyl & Datta, Susmita & Datta, Somnath, 2008. "clValid: An R Package for Cluster Validation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i04).
    12. Gustavo Dambiski Gomes de Carvalho & Luis Mauricio Martins de Resende & Joseane Pontes & Hélio Gomes de Carvalho & Leozenir Mendes Betim, 2021. "Innovation and Management in MSMEs: A Literature Review of Highly Cited Papers," SAGE Open, , vol. 11(4), pages 21582440211, October.
    13. Beynon, Malcolm J. & Jones, Paul & Pickernell, David, 2023. "Evaluating EU-Region level innovation readiness: A longitudinal analysis using principal component analysis and a constellation graph index approach," Journal of Business Research, Elsevier, vol. 159(C).
    14. Fišar, Miloš & Greiner, Ben & Huber, Christoph & Katok, Elena & Ozkes, Ali & Management Science Reproducibility Collaboration, 2023. "Reproducibility in Management Science," Department for Strategy and Innovation Working Paper Series 03/2023, WU Vienna University of Economics and Business.
    15. Greco, Marco & Germani, Francesca & Grimaldi, Michele & Radicic, Dragana, 2022. "Policy mix or policy mess? Effects of cross-instrumental policy mix on eco-innovation in German firms," Technovation, Elsevier, vol. 117(C).
    16. Chrystalla Kapetaniou & Soo Hee Lee, 2019. "Geographical proximity and open innovation of SMEs in Cyprus," Small Business Economics, Springer, vol. 52(1), pages 261-276, January.
    17. Pełka Marcin, 2019. "Analysis of Happiness in EU Countries Using the Multi-Model Classification based on Models of Symbolic Data," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 23(3), pages 15-25, September.
    18. Axel Strauß & François Guilhaumon & Roger Daniel Randrianiaina & Katharina C Wollenberg Valero & Miguel Vences & Julian Glos, 2016. "Opposing Patterns of Seasonal Change in Functional and Phylogenetic Diversity of Tadpole Assemblages," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-18, March.
    19. Florencia Fiorentin & Mariano Pereira & Diana Suárez, 2020. "The relationship between public funds, innovation and employment among Argentinean manufacturing firms," Journal of Evolutionary Economics, Springer, vol. 30(3), pages 773-791, July.
    20. Thomas Reutterer & Kurt Hornik & Nicolas March & Kathrin Gruber, 2017. "A data mining framework for targeted category promotions," Journal of Business Economics, Springer, vol. 87(3), pages 337-358, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:eaiada:v:22:y:2018:i:3:p:84-98:n:6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.