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2020 | vol. 20, iss. 1 | 319--340
Tytuł artykułu

Clustering Poland Among Eu Countries in Terms of a Sustainable Development Level in the Light of Various Cluster Stability Measures

Autorzy
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Research background: Recently in the context of taxonomy methods a lot of attention has been paid to the issue of stability of these methods, i.e. the answer to the question: do the groups that were created as a result of clustering really occur (the structure is stable), or did they appear accidentally. Purpose: The article is inspired by the Reviewers of the author's previous publications on this subject and will be a summary of research to date which has followed two paths. On one hand, they recognize ways of measuring cluster stability proposed in the literature (e.g. Rozmus, 2017). On the other, they use these measures to cluster Poland among the EU members in terms of sustainable development level (e.g. Rozmus, 2019). Research methodology: The literature proposes a number of different ways for measuring stability. Theoretical considerations have also led to the development of computer tools for the practical implementation of the proposed ways to study stability. The practical tools are available within several R packages, e.g.: clv, clValid, fpc, which are used in this research Results: The results, however, showed that different measures of stability lead to different results. Novelty: The innovation of this approach is the use of stability measures to such a problem (i.e. clustering EU members in terms of the sustainable development level). In addition, the article will report a synthesis and comparative analysis of the results obtained using various stability measures. (original abstract)
Rocznik
Strony
319--340
Opis fizyczny
Twórcy
  • University of Economics in Katowice, Poland
Bibliografia
  • Ben-Hur, A., Guyon, I. (2003). Detecting stable clusters using principal component analysis. Methods in Molecular Biology. 224, 59-182.
  • Borys, T. (ed.) (2005). Wskaźniki zrównoważonego rozwoju. Warszawa-Białystok: Wydawnictwo Ekonomia i Środowisko.
  • Borys, T. (2014). Wybrane problemy metodologii pomiaru nowego paradygmatu rozwoju - polskie doświadczenia. Optimum. Studia Ekonomiczne, 3 (69), 3-21.
  • Brock, G., Pihur, V., Datta, S., Datta, S. (2008). clValid: an R package for cluster validation. Journal of Statistical Software, 25 (4). Retrieved from: http://www.jstatsoft.org/v25/i04.
  • Fang, Y., Wang, J. (2012). Selection of the number of clusters via the bootstrap method. Computational Statistics and Data Analysis, 56, 468-477.
  • Henning, C. (2007). Cluster-wise assessment of cluster stability. Computational Statistics and Data Analysis, 52, 258-271.
  • Lord, E., Willems, M., Lapointe, F.J., Makarenkov, V. (2017). Using the stability of objects to determine the number of clusters in datasets. Information Sciences, 393, 29-46.
  • Lorek, E. (2011). Ekonomia zrównoważonego rozwoju w badaniach polskich i niemieckich. Studia Ekonomiczne, Zeszyty Naukowe Uniwersytetu Ekonomicznego w Katowicach, 90, 103-112.
  • Kronthaler, F. (2005). Economic capability of East German regions: Results of a cluster analysis. Regional Studies, 39 (6): 739-750.
  • Marino, V., Presti, L.L. (2019). Stay in touch! New insights into end-user attitudes towards engagement platforms. Journal of Consumer Marketing, 36, 772-783.
  • Repkine, A. (2012). How similar are the east asian economies? A cluster analysis perspective on economic cooperation in the region. Journal of International and Area Studies, 19 (1), 27-44.
  • Rozmus, D. (2017). Using R packages for comparison of cluster stability. Acta Universitatis Lodziensis Folia Oeconomica, 330 (4), 77-86.
  • Rozmus, D. (2019). Poziom zrównoważonego rozwoju w Polsce i krajach UE - analiza z zastosowaniem miar stabilności grupowania. Przegląd Statystyczny, LXVI (1), 84-93.
  • Shamir, O., Tishby, N. (2008). Cluster stability for finite samples. Advances in Neural Information Processing Systems, 20, 1297-1304.
  • Shubat, O., Bagirova, A, Makhabat, A., Ivlev, A. (2016). The use of cluster analysis for demographic policy development: evidence from Russia (pp. 159-165). 30th European Conference on Modelling and Simulation.
  • Simpach, O. (2013). Application of cluster analysis on the demographic development of municipalities in the districts of liberecky region (pp. 1390-1399). Conference Proceedings of the 7th International Days of Statistics and Economics.
  • Suzuki, R., Shimodaira, H. (2006). Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics, 22 (12), 1540-1542.
  • Volkovich, Z., Barzily, Z., Toledano-Kitai, D., Avros, R. (2010). The Hotteling's metric as a cluster stability index. Computer Modelling and New Technologies, 14 (4), 65-72.
  • Wang, J. (2010). Consistent selection of the number of clusters via cross-validation. Biometrika, 97, 893-904.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171599515

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