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The Digital Diversity in Russian Regional Dynamics: Analysis by Machine Learning Methods. / Войтенко, Сергей Семенович; Гадасина, Людмила Викторовна; Luukka, Pasi.

в: Nordic Journal of Business, Том 69, № 1, 2020, стр. 7-20.

Результаты исследований: Научные публикации в периодических изданияхстатьяРецензирование

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@article{49a293e0d1a74f77af313d5c212a328d,
title = "The Digital Diversity in Russian Regional Dynamics: Analysis by Machine Learning Methods",
abstract = "The paper focuses on the digital economy of the Russian Federation by analyzing the level of digitization of its regions based on official statistical data from open sources using machine learning methods with verification of the most strongly influencing factors. Hierarchical clustering is applied to determine different groups of regions. Random Forest classification algorithm enabled us to explain the peculiar properties of the different regional groups.",
keywords = "Russian regions, Federal state statistics, cluster analysis, random forest algorithm, digitalization",
author = "Войтенко, {Сергей Семенович} and Гадасина, {Людмила Викторовна} and Pasi Luukka",
year = "2020",
language = "English",
volume = "69",
pages = "7--20",
journal = "Nordic Journal of Business",
issn = "2342-9003",
publisher = "Association of Business Schools Finland",
number = "1",

}

RIS

TY - JOUR

T1 - The Digital Diversity in Russian Regional Dynamics: Analysis by Machine Learning Methods

AU - Войтенко, Сергей Семенович

AU - Гадасина, Людмила Викторовна

AU - Luukka, Pasi

PY - 2020

Y1 - 2020

N2 - The paper focuses on the digital economy of the Russian Federation by analyzing the level of digitization of its regions based on official statistical data from open sources using machine learning methods with verification of the most strongly influencing factors. Hierarchical clustering is applied to determine different groups of regions. Random Forest classification algorithm enabled us to explain the peculiar properties of the different regional groups.

AB - The paper focuses on the digital economy of the Russian Federation by analyzing the level of digitization of its regions based on official statistical data from open sources using machine learning methods with verification of the most strongly influencing factors. Hierarchical clustering is applied to determine different groups of regions. Random Forest classification algorithm enabled us to explain the peculiar properties of the different regional groups.

KW - Russian regions

KW - Federal state statistics

KW - cluster analysis

KW - random forest algorithm

KW - digitalization

M3 - Article

VL - 69

SP - 7

EP - 20

JO - Nordic Journal of Business

JF - Nordic Journal of Business

SN - 2342-9003

IS - 1

ER -

ID: 58559662