Oleksandr Valkov

Bio: Oleksandr Valkov is an academic researcher from Khmelnytskyi National University. The author has contributed to research in topics: Fuzzy clustering & Regional policy. The author has an hindex of 2, co-authored 3 publications receiving 7 citations.

Analysis of regional development disparities in Ukraine with fuzzy clustering technique

Abstract: Disparities in the development of regions in any country affect the entire national economy. Detecting the disparities can help formulate the proper economic policies for each region by taking action against the factors that slow down the economic growth. This study was conducted with the aim of applying clustering methods to analyse regional disparities based on the economic development indicators of the regions of Ukraine. There were considered fuzzy clustering methods, which generalize partition clustering methods by allowing objects to be partially classified into more than one cluster. Fuzzy clustering technique was applied using R packages to the data sets with the statistic indicators concerned to the economic activities in all administrative regions of Ukraine in 2017. Sets of development indicators for different sectors of economic activity, such as industry, agriculture, construction and services, were reviewed and analysed. The study showed that the regional cluster classification results strongly depend on the input development indicators and the clustering technique used for this purpose. Consideration of different partitions into fuzzy clusters opens up new opportunities in developing recommendations on how to differentiate economic policies in order to achieve maximum growth for the regions and the entire country.

Application of Fuzzy Clustering to Shaping Regional Development Strategies in Ukraine

Abstract: 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.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Application of Fuzzy Clustering to Shaping Regional Development Strategies in Ukraine

Abstract: 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.

Using Multidimensional Scaling for Assessment Economic Development of Regions

Abstract: Addressing socio-economic development issues are strategic and most important for any country. Multidimensional statistical analysis methods, including comprehensive index assessment, have been successfully used to address this challenge, but they don't cover all aspects of development, leaving some gap in the development of multidimensional metrics. The purpose of the study is to construct a latent metric space based on the use of multidimensional scaling. Based on statistics showing the economic development of Ukrainian regions, two-dimensional space of latent scales was constructed and Ukrainian's regions were positioned in this space. The results were interpreted meaningfully. This use of multidimensional statistical analysis confirms its usefulness for measuring the economic development of regions and allows their comprehensive assessment and comparison.

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

Abstract: 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.

Regional Economy Condition in Indonesia during COVID-19 Pandemic: An Analysis using Teaching Learning-Based Fuzzy Geodemographic Clustering

Abstract: COVID-19 has impacted Indonesia and caused an economic recession during 2020. The economic condition in Indonesia should be evaluated through the regional economic condition. One well-known approach to do a regional analysis is a geodemographic analysis using Fuzzy Geographically Weighted Clustering (FGWC). However, FGWC is still weak against the local optima, so it is necessary to use an optimisation algorithm to enhance it. This study proposes a new approach of FGWC enhancement using Elicit Teaching-Learning Based Optimisation (ETLBO) to analyse the regional economic condition in Indonesia. We compare ETLBO with previously implemented optimisation algorithms in FGWC, such as Particle Swarm Optimisation (PSO) and Intelligent Firefly Algorithm (IFA). This study found that ETLBO performs well in identifying Indonesia’s regional economic condition. Moreover, the clustering results showed the difference of problematic sectors. We also found that the provinces in Java Island joined into a cluster and have problems in many sectors. This study can be used as the basis for the evaluation of regional economic conditions in Indonesia.