<abstract xmlns="http://www.w3.org/1999/xhtml">The <bold>research background</bold> 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 (<italic>human development index</italic>), 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.<bold>The main purpose</bold> 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.<bold>Research methodology</bold> covers linear ordering with the application of multidimensional scaling for a visualisation of results and ensemble clustering for symbolic data.<bold>The results</bold> 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.<bold>The novelty</bold> 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.</abstract>

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.

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

Faculty of Economics, Management and Tourism, Department of Econometrics and Computer Sciences

Folia Oeconomica Stetinensia

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

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...

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

Published Online: Dec 26, 2019

Page range: 117 - 133

Received: Nov 13, 2018

Accepted: Oct 15, 2019

DOI: https://doi.org/10.2478/foli-2019-0017

Keywords
country development, symbolic data analysis, OECD countries, ensemble clustering

© 2019 Marcin Pełka, published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

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

Published Online: Dec 26, 2019

Page range: 117 - 133

Received: Nov 13, 2018

Accepted: Oct 15, 2019

DOI: https://doi.org/10.2478/foli-2019-0017

Keywordscountry development, symbolic data analysis, OECD countries, ensemble clustering

© 2019 Marcin Pełka, published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Keywords
country development, symbolic data analysis, OECD countries, ensemble clustering