Assessment of the Development of the European Oecd Countries with the Application of Linear Ordering and Ensemble Clustering of Symbolic Data

• Autorzy: Marcin Pełka
• Języki publikacji: EN

Abstrakty

EN

The research background of the paper covers the development of a country, that can be measured in various ways. Simple indicators, like GDP and also complex indicators such as HDI (human development index), can be used to measure country development. However, usually countries are divided into groups via setting some arbitrary levels of final measure. What is more, the composite (complex) indices have some problems and errors. The main purpose of the paper is the assessment of the development of the selected European OECD countries with the application of the linear ordering and ensemble clustering of symbolic data as well as comparison of the ensemble clustering with a single model. Research methodology covers linear ordering with the application of multidimensional scaling for a visualisation of results and ensemble clustering for symbolic data. The results are compared according to adjusted Rand and silhouette indices. The obtained results show that ensemble clustering for symbolic data can be a useful tool in country development analysis and allows reaching better results than a single model. The novelty of the proposed approach is to use a cluster analysis to obtain the clusters of countries with similar variables' values (indicators of development) and the application of multidimensional scaling for symbolic data in order to visualise linear ordering results. (original abstract)

Słowa kluczowe

EN

Data analysis; Economic development of the country; Business cluster

PL

Analiza danych; Rozwój gospodarczy państwa; Klastry

• Czasopismo: Folia Oeconomica Stetinensia
• Rocznik: 2019
• Numer: vol. 19, iss. 2
• Strony: 117--133

Twórcy

autor

Marcin Pełka

• Wrocław University of Economics, Poland

Bibliografia

• Alonso, D.B., Androniceanu, A., Georgescu, I. (2016). Sensitivity and vulnerability of European countries in time of crisis based on a new approach to data clustering and curvilinear analysis. Administratie si Management Public, 27, 46.
• Aziz, S.A., Amin, R.M., Yusof, S.A., Haneef, M.A., Mohamed, M.O., Oziev, G. (2015). A critical analysis of development indices. Australian Journal of Sustainable Business and Society, 1 (01).
• Baker, B. (2011). World development: An essential text. New Internationalist.
• Bates, W. (2009). Gross national happiness. Asian-Pacific Economic Literature, 23 (2), 1-16.
• Bock, H.H., Diday, E. (eds.) (2012). Analysis of symbolic data: exploratory methods for extracting statistical information from complex data. Springer Science & Business Media.
• Billard, L., Diday, E. (2006). Symbolic Data Analysis: Conceptual Statistics and Data Mining John Wiley.
• Brito, P. (2002). Hierarchical and pyramidal clustering for symbolic data. Journal of the Japanese Society of Computational Statistics, 15 (2), 231-244.
• Brito, P. (1995). Symbolic objects: order structure and pyramidal clustering. Annals of Operations Research, 55 (2), 277-297.
• Dasgupta, S., Wheeler, D., Mody, A., Roy, S. (1999). Environmental regulation and development: A cross-country empirical analysis. The World Bank.
• De Carvalho, F.D.A., Lechevallier, Y., De Melo, F.M. (2012). Partitioning hard clustering algorithms based on multiple dissimilarity matrices. Pattern Recognition, 45 (1), 447-464.
• Demirgüç-Kunt, A., Levine, R. (eds.) (2004). Financial structure and economic growth: A crosscountry comparison of banks, markets, and development. MIT press.
• Diday, E., Noirhomme-Fraiture, M. (eds.) (2008). Symbolic data analysis and the SODAS software. John Wiley & Sons.
• Dijkstra, A.G., Hanmer, L.C. (2000). Measuring socio-economic gender inequality: Toward an alternative to the UNDP gender-related development index. Feminist economics, 6 (2), 41-75.
• Dudoit, S., Fridlyand, J. (2003). Bagging to improve the accuracy of a clustering procedure. Bioinformatics, 19 (9), 1090-1099.
• Durand, M. (2015). The OECD better life initiative: How's life? and the measurement of wellbeing. Review of Income and Wealth, 61 (1), 4-17.
• Fred, A.L., Jain, A.K. (2005). Combining multiple clusterings using evidence accumulation. IEEE Transactions on Pattern Analysis & Machine Intelligence, 6, 835-850.
• Gatnar, E., Walesiak, M. (2011). Analiza danych jakościowych i symbolicznych z wykorzystaniem programu R. Warszawa: C.H. Beck.
• Ghaemi, R., Sulaiman, M.N., Ibrahim, H., Mustapha, N. (2009). A survey: clustering ensembles techniques. World Academy of Science, Engineering and Technology, 50, 636-645.
• Groenen, P., Terada, Y. (2015). Symbolic Multidimensional Scaling (No. EI 2015-15).
• Groenen, P.J., Winsberg, S., Rodriguez, O., Diday, E. (2006). I-Scal: Multidimensional scaling of interval dissimilarities. Computational Statistics & Data Analysis, 51 (1), 360-378.
• Groenen, P.J.F., Winsberg, S., Rodriguez, O., Diday, E. (2005). SymScal: symbolic multidimensional scaling of interval dissimilarities (No. EI 2005-15). Econometric Institute Research Papers.
• Hellwig, Z. (1981). Wielowymiarowa analiza porównawcza i jej zastosowanie w badaniach wielocechowych obiektów gospodarczych. In: W. Welfe (ed.), Metody i modele ekonomiczno-matematyczne w doskonaleniu zarządzania gospodarką socjalistyczną (pp. 46-68). Warszawa: PWE.
• Hornik, K. (2005). A CLUE for CLUster ensembles. Journal of Statistical Software, 14 (12), 1-25.
• Hsu, P.H., Tian, X., Xu, Y. (2014). Financial development and innovation: Cross-country evidence. Journal of Financial Economics, 112 (1), 116-135.
• Kaufman, L., Rousseeuw, P.J. (2009). Finding groups in data: an introduction to cluster analysis (Vol. 344). John Wiley & Sons.
• Ketels, C.H., Memedovic, O. (2008). From clusters to cluster-based economic development. International Journal of Technological Learning, Innovation and Development, 1 (3), 375-392.
• Leisch, F. (1999). Bagged clustering. Working Paper no. 51. Vienna University of Economics and Business Administration.
• Liapis, K., Rovolis, A., Galanos, C., Thalassinos, E. (2013). The Clusters of Economic Similarities between EU Countries: A View Under Recent Financial and Debt Crisis. European Research Studies, 16 (1).
• Magee, L., Scerri, A., James, P. (2012). Measuring social sustainability: A community-centred approach. Applied Research in Quality of Life, 7 (3), 239-261.
• Mercan, B., Goktas, D. (2011). Components of innovation ecosystems: a cross-country study. International research journal of finance and economics, 76 (16), 102-112.
• McGillivray, M. (1991). The human development index: yet another redundant composite development indicator? World Development, 19 (10), 1461-1468.
• Nayak, P. (2010). Human development: conceptual and measurement issues. In: P. Nayak (ed.), Growth and Human Development in North East India (pp. 3-18). New Delhi: Oxford University Press.
• Noirhomme-Fraiture, M., Brito, P. (2011). Far beyond the classical data models: symbolic data analysis. Statistical Analysis and Data Mining: the ASA Data Science Journal, 4 (2), 157-170.
• Pełka, M. (2017). Klasyfikacja wielomodelowa danych symbolicznych w badaniu innowacyjności krajów Unii Europejskiej. Ekonometria, 2 (56), 42-51.
• Pełka, M. (2018). Analysis of Innovations in the European Union Via Ensemble Symbolic Density Clustering. Econometrics, 22 (3), 84-98.
• Pełka, M. (2015). An adaptation of COBWEB for symbolic data case. Statistica, 75 (3), 265-273
• Sen, A. (1999). Freedom as development. New York: Oxford Univerity Press.
• Sagar, A.D., Najam, A. (1998). The human development index: a critical review. Ecological economics, 25 (3), 249-264.
• Sen, A. (1994). Human Development Index: Methodology and Measurement.
• Stanton, E.A. (2007). The human development index: A history. PERI Working Papers, 85.
• Vachon, S., Mao, Z. (2008). Linking supply chain strength to sustainable development: a country- level analysis. Journal of Cleaner Production, 16 (15), 1552-1560.
• Verde, R. (2004). Clustering methods in symbolic data analysis. In: Classification, clustering, and data mining applications (pp. 299-317). Berlin, Heidelberg: Springer.
• Voigt, S. (2009). The effects of competition policy on development-cross-country evidence using four new indicators. Journal of Development Studies, 45 (8), 1225-1248.
• Walesiak, M. (2016). Visualization of linear ordering results for metric data with the application of multidimensional scaling. Ekonometria, 2 (52), 9-21.
• Walesiak, M. (2017). Wizualizacja wyników porządkowania liniowego dla danych porządkowych z wykorzystaniem skalowania wielowymiarowego. Przegląd Statystyczny, 64 (1), 5-19.
• Walesiak, M. (2017). The application of multidimensional scaling to measure and assess changes in the level of social cohesion of the Lower Silesia region in the period 2005-2015. Econometrics/Ekonometria, 3 (57).
• Walesiak, M., Dudek, A. (2018). The mdsOpt package for R software. Retrieved from: www.rproject.org.

Identyfikatory

DOI

10.2478/foli-2019-0017