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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">voprstat</journal-id><journal-title-group><journal-title xml:lang="ru">Вопросы статистики</journal-title><trans-title-group xml:lang="en"><trans-title>Voprosy Statistiki</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2313-6383</issn><issn pub-type="epub">2658-5499</issn><publisher><publisher-name></publisher-name></publisher></journal-meta><article-meta><article-id custom-type="elpub" pub-id-type="custom">voprstat-452</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>МАТЕМАТИКО-СТАТИСТИЧЕСКИЕ МЕТОДЫ В АНАЛИЗЕ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>MATHEMATICAL AND STATISTICAL METHODS IN ANALYSIS</subject></subj-group></article-categories><title-group><article-title>КЛАСТЕРНЫЙ АНАЛИЗ В ИССЛЕДОВАНИИ РЕГИОНАЛЬНОЙ ДИФФЕРЕНЦИАЦИИ ПРОЦЕССОВ ВОСПРОИЗВОДСТВА МОЛОДОГО ПОКОЛЕНИЯ В РОССИИ</article-title><trans-title-group xml:lang="en"><trans-title>THE USE OF CLUSTER ANALYSIS IN THE STUDY OF REGIONAL DIFFERENTIATION OF RUSSIAN YOUNG GENERATION REPRODUCTION</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Шубат</surname><given-names>О. М.</given-names></name><name name-style="western" xml:lang="en"><surname>Shubat</surname><given-names>Oksana M.</given-names></name></name-alternatives><bio xml:lang="ru"><p>канд. экон. наук, доцент</p><p>(г. Екатеринбург, Россия).</p></bio><bio xml:lang="en"><p>(Ekaterinburg, Russia)</p></bio><email xlink:type="simple">o.m.shubat@urfu.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Караева</surname><given-names>А. П.</given-names></name><name name-style="western" xml:lang="en"><surname>Karaeva</surname><given-names>Anzhelika P.</given-names></name></name-alternatives><bio xml:lang="ru"><p>студент</p><p>(г. Екатеринбург, Россия).</p></bio><bio xml:lang="en"><p>(Ekaterinburg, Russia)</p></bio><email xlink:type="simple">anzhelika.karaeva@gmail.com</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Высшая школа экономики и менеджмента Уральского федерального университета</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Ural Federal University</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2017</year></pub-date><pub-date pub-type="epub"><day>02</day><month>03</month><year>2017</year></pub-date><volume>0</volume><issue>2</issue><fpage>48</fpage><lpage>59</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Шубат О.М., Караева А.П., 2017</copyright-statement><copyright-year>2017</copyright-year><copyright-holder xml:lang="ru">Шубат О.М., Караева А.П.</copyright-holder><copyright-holder xml:lang="en">Shubat O.M., Karaeva A.P.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://voprstat.elpub.ru/jour/article/view/452">https://voprstat.elpub.ru/jour/article/view/452</self-uri><abstract><p>Исторически регионы России существенно различаются между собой по целому ряду социально-экономических показателей. Очевидно, что в этих условиях для решения демографических проблем российских территорий применение единого, уравнительного подхода не может быть эффективным. Здесь необходим дифференцированный подход, основанный на выделении групп регионов, отличающихся схожими особенностями сложившейся демографической ситуации.Цель настоящего исследования - выявление региональной дифференциации процессов воспроизводства молодого поколения в России на основе кластерного анализа. Для выделения групп регионов, характеризующихся схожими тенденциями в этой сфере, применялся метод агломеративной иерархической кластеризации. В результате анализа были выделены пять сегментов, различающихся спецификой воспроизводства молодого поколения.Проведенное исследование позволило сформулировать ряд выводов теоретического и прикладного характера, нацеленных на совершенствование реализуемых в стране мер государственной молодежной политики. Кроме того, исследование показало, что статистический инструментарий кластерного анализа может рассматриваться в качестве эффективного механизма создания практических рекомендаций для оперативного решения возникающих в молодежной среде проблем и поэтому может быть интегрирован в исследовательскую составляющую государственной молодежной политики.</p></abstract><trans-abstract xml:lang="en"><p>Historically, there have been significant differences between the various parts of Russia as regards socio-economic parameters. Clearly, the use of a single set of measures to address demographic problems in different parts of Russia cannot be effective. This situation requires a differentiated approach based on the allocation of groups of regions that are similar in features of the current demographic situation. The purpose of the study was to identify the regional differentiation of the processes of young generation reproduction. The authors performed an agglomerative hierarchical clustering algorithm to identify groups of regions characterised by similar features in the sphere of reproduction. The analysis enabled to reveal five groups of regions where the situation with young population has characteristic features.The study has allowed drawing a number of theoretical and practical conclusions aimed at improving the country’s state youth policy. In addition, this research has shown that statistical cluster analysis can be considered as an effective tool of development of practical recommendations for prompt solving problems among youth. Therefore, this type of analysis can be integrated as an analytical tool in the research component of the state youth policy.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>кластерный анализ</kwd><kwd>проблемы молодежи</kwd><kwd>регионы России</kwd><kwd>молодежная политика</kwd></kwd-group><kwd-group xml:lang="en"><kwd>cluster analysis</kwd><kwd>youth issues</kwd><kwd>Russian regions</kwd><kwd>youth policy</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Илышев А.М., Шубат О.М. Многомерная классификация данных: особенности методики, анализ практики и перспектив применения // Вопросы статистики. 2010. № 10. С. 34-40.</mixed-citation><mixed-citation xml:lang="en">Ilyshev A.M., Shubat O.M. 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