Analysis of regional development disparities in Ukraine with fuzzy clustering technique

Gorbatiuk, Kateryna; Mantalyuk, Olha; Proskurovych, Oksana; Valkov, Oleksandr

doi:10.1051/shsconf/20196504008

Cited by 8 publications

(14 citation statements)

References 20 publications

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“…Basic fuzzy clustering methods are: fuzzy clustering based on fuzzy relation, fuzzy clustering based on objective functions, and the fuzzy generalized K-nearest neighbour rule [6]. For most of fuzzy clustering algorithms, the number of clusters must be defined before applying the method.…”

Section: Methodsmentioning

confidence: 99%

“…This research is an extension of the study [6] on the problem of clustering Ukrainian regions accordingly to their economic development levels. There were presented the new approach to assessing regional disparities in Ukraine using a fuzzy clustering technique.…”

Section: Literature Reviewmentioning

confidence: 99%

“…There were presented the new approach to assessing regional disparities in Ukraine using a fuzzy clustering technique. The statistical data on quantitative indicators of economic activities in different regions of Ukraine in 2017 were considered in [6], and it were used to obtain different partitions of the regions into fuzzy clusters.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The problems of estimating and classifying of regional development disparities, and some approaches to assessing the economic development levels, are discussed in [6]- [11]. Also, different clustering techniques, including fuzzy approaches, based on fuzzy sets theory of L. Zadeh [12], are presented in [13]- [18].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Our research was conducted on the same data set as in [6]. The crisp clustering results and fuzzy clustering results are used for further study of development disparities of the regions of Ukraine to outline the ways of using this technique in shaping regional development strategies.…”

Section: Case Study: Detecting the Development Disparities Using mentioning

confidence: 99%

See 4 more Smart Citations

Application of Fuzzy Clustering to Shaping Regional Development Strategies in Ukraine

Gorbatiuk¹,

Mantalyuk²,

Proskurovych³

et al. 2019

Proceedings of the 6th International Conference on Strategies, Models and Technologies of Economic Systems Management (SMTESM 2

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Formation of regional development strategies is impossible without taking into account the disparities in the development levels of regions which affect the entire national economy. Strengths and weaknesses of the functioning of each regional economic system, favourable and negative trends in the development of regions, disparities in the development levels, should be analysed and detected. This paper is devoted to applying of fuzzy clustering methods for the purpose of differentiation of development levels of the regions of Ukraine, in order to make recommendations on using of fuzzy clustering results in shaping regional development strategies. The fuzzy clustering methods were applied to the regional economic development indicators of the regions of Ukraine in 2017. There were considered different fuzzy clustering results and clustering validity techniques. The study presents the fuzzy classifications accompanied with the cluster validity process and substantial analysis of the economic indicators. The results of fuzzy clustering allow to consider in more detail the similarities in the regional economic development levels, assigned to the same clusters, and reveal the dissimilarities between the regions assigned to the different clusters. The membership coefficients can be used as values for regulation of influence on important economic components of the regional development strategies.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Case Study: Detecting the Development Disparities Using mentioning

confidence: 99%

See 3 more Smart Citations

Application of Fuzzy Clustering to Shaping Regional Development Strategies in Ukraine

Gorbatiuk¹,

Mantalyuk²,

Proskurovych³

et al. 2019

Proceedings of the 6th International Conference on Strategies, Models and Technologies of Economic Systems Management (SMTESM 2

Self Cite

View full text Add to dashboard Cite

show abstract

Using a comprehensive index technology to analyze structural changes in the regions’ economic development in a COVID-19 pandemic: the case of Ukraine

2021

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The paper investigates the issues of evaluating structural changes in the regions’ economic development based on the comprehensive index assessment technology. The impact of the COVID-19 pandemic on regional development and changes in the regional structure is considered. The authors propose the use of block convolution to design a comprehensive index based on a set of metric initial indicators that characterize the regions’ economic development. Grouping the set of initial indicators is carried out based on the method of an extreme grouping of parameters and the method of principal components. A weighted linear additive convolution was used to develop partial composite indices and an economic development comprehensive index. The practical approbation was carried out for the regions of Ukraine according to the data of 9 months of 2019 and the same period of 2020. To establish the regions’ structure, we used the division of the comprehensive index values into intervals and further distributing regions into classes according to the level of economic development. There is a general decrease in the value of the integrated indicator in 2020, caused by the impact of the COVID-19 pandemic. However, no significant changes in the structure of the regions were detected, which indicates an equally negative impact of the pandemic for all regions of Ukraine.

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Assessment of Regions’ Socio-Economic Development Based on Bon-Metric Data

Hryhoruk

Хрущ

Grygoruk

2021

2021 11th International Conference on Advanced Computer Information Technologies (ACIT)

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Assessment of socio-economic development of both separate territories and countries as a whole, comparison of the received estimations to identify the development disproportions, lacks, and gaps, application of the best practices to decide the revealed problems promotes the development of balanced state regional development policy. Quite common tasks of such assessment are the ranking of regions and their grouping according to some generalized socio-economic development characteristics. The solution to these problems is complicated by the use of a set of indicators of non-numerical nature because traditional statistical methods are unsuitable for their processing. The paper considers the solution to the problem of constructing a consensus rank based on Kemeny's median. To solve the problem of grouping regions, an approach is proposed based on the picture of regions in the space of partial consensus rankings for economic and social development separately, with the subsequent detection of object stratification in this space. The practical implementation of the proposed approach according to partial rankings is given. A heuristic algorithm was used to construct Kemeny's median. The obtained grouping of the regions of Ukraine reproduced as a whole their order following the resulting consensus ranking. Comparison of the obtained results with the known results of solving such tasks, calculated on the set of metric indicators also showed their consistency.

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Analysis of regional development disparities in Ukraine with fuzzy clustering technique

Cited by 8 publications

References 20 publications

Application of Fuzzy Clustering to Shaping Regional Development Strategies in Ukraine

Application of Fuzzy Clustering to Shaping Regional Development Strategies in Ukraine

Using a comprehensive index technology to analyze structural changes in the regions’ economic development in a COVID-19 pandemic: the case of Ukraine

Assessment of Regions’ Socio-Economic Development Based on Bon-Metric Data

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