Pattern Analysis and Clustering in the Study of State Capacity: «Adaptive Optics» for Political Science

The central focus of this paper is a methodological one. Using the set of indicators of state capacity, we demonstrate a specific strategy for identifying sustainable structures in multidimensional data sets that reflect complex and ambiguous concepts of political science. A key feature of this strategy is the application of related, but significantly different technically, multidimensional methods - cluster and pattern analyses. We use hierarchical clustering with various combinations of metrics and amalgamation rules, as well as ordinal-invariant pattern-clustering. Properties of pattern analysis as a method for studying multidimensional data are shown for the first time (to the best of our knowledge) in the political science literature. Since clustering has been actively used in political science for a long time, pattern analysis is still practically not adopted in our science. This situation requires correction, since pattern-analysis has some important and in many ways unique capabilities. It was shown that the combination of pattern and cluster analyses makes it possible to identify consistent structures that have a clear interpretation in terms of political science. Thus, in the course of our study, several types of state capacity were identified (although this task was rather illustrative for us). We use a set of empirical indicators of state capacity: the share of military spending in GDP, the share of military personnel in the total population, the share of tax revenues in GDP, the total rate of homicides and victims of internal conflicts, and the quality of government institutions. Data for more than 150 countries are taken for 1996, 2005 and 2015. Stable combinations of the values of these indicators, identified simultaneously via pattern and cluster analyses, form the structures of state capacity. In conclusion, we show the most promising directions for the development of the methodology described in this paper. One of the most important is the analysis of the dynamics of countries within the pattern-cluster structures of state capacity.

государственная состоятельность, кластер, кластерный анализ, паттерн, анализ паттернов, порядково-инвариантная паттерн-кластеризация, state capacity, cluster, cluster analysis, pattern, pattern analysis, ordinal-invariant pattern-clustering

1. Анализ данных науки, образования и инновационной деятельности с использованием методов анализа паттернов: препринт WP7/2012/07 / Ф.Т. Алескеров, Л.М. Гохберг, Л.Г. Егорова, А.Л. Мячин, Г.С. Сагиева; Нац. исслед. ун-т «Высшая школа экономики». - М.: Изд. дом Высшей школы экономики, 2012. - 72 с. - Режим доступа: https://wp.hse.ru/data/2013/01/22/1305726760/ WP7_2012_07_f_.pdf (Дата обращения: 15.05.2019.)

2. Анализ паттернов в статике и динамке, часть 1: обзор литературы и уточнение понятия / Ф.Т. Алескеров, В.Ю. Белоусова, Л.Г. Егорова, Б.Г. Миркин // Бизнес-информатика. - М., 2013. - № 3(25). - С. 3-18.

3. Ахременко А.С., Горельский И.Е., Мельвиль А.Ю. Как и зачем измерять и сравнивать государственную состоятельность различных стран мира? Теоретико-методологические основания // Полис. Политические исследования. - М., 2019 a. - № 2. - С. 8-23. - DOI: https://doi.org/10.17976/jpps/2019.02.02

4. Ахременко А.С., Горельский И.Е., Мельвиль А.Ю. Как и зачем измерять и сравнивать государственную состоятельность различных стран мира? Опыт эмпирического исследования // Полис. Политические исследования. - М., 2019 b. - № 3. - С. 49-68. - DOI: https://doi.org/10.17976/jpps/2019.03.04

5. Миркин Б.Г. Методы кластер-анализа для поддержки принятия решений: обзор: препринт WP7/2011/03. - М.: Изд. дом Национального исследовательского университета «Высшая школа экономики», 2011. - 88 с. - Режим доступа: https://www.hse.ru/data/2011/05/19/1213868030/WP7_2011_03f.pdf (Дата помещения: 15.05.2019.)

6. Мячин А.Л. Анализ паттернов в системе параллельных координат на базе парного сравнения показателей // Автоматика и телемеханика. - М., 2019 а. - № 1. - С. 138-152. - DOI: https://doi.org/10.1134/S0005231019010100

7. Мячин А.Л. Анализ паттернов: диффузионно-инвариантная паттерн-кластеризация // Проблемы управления. - 2016. - № 4. - С. 2-9.

8. Мячин А.Л. Определение центроидов для повышения точности порядково-инвариантной паттерн-кластеризации // Управление большими системами.-2019 b. - № 78. - C. 6-22.

9. Aleskerov F., Ersel H., Yolalan R. Clustering Turkish commercial banks according to structural similarities // Yapi Kredi Research Department Discussion Paper Series. - Istanbul, 1997. - P. 97-102.

10. Charron N. Diverging Cohesion? Globalisation, State Capacity and Regional Inequalities Within and Across European Countries // European Urban and Regional Studies. - L., 2016. - Vol. 23, N 3. - P. 355-373.

11. Dubes R., Jain A.K. Clustering techniques: the user's dilemma // Pattern Recognition. - N.Y., 1976. - Т. 8, N 4. - P. 247-260.

12. Hanson J., Sigman R. Leviathan’s Latent Dimensions: Measuring State Capacity for Comparative Political Research. - 2013. - Mode of access: http://www-personal.umich.edu/~jkhanson/resources/hanson_sigman13.pdf (accessed: 20.04.2019.)

13. Hendrix C. Measuring State Capacity: Theoretical and Empirical Implications for the Study of Civil Conflict // Journal of Peace Research. - L., 2010. - Vol. 47, N. 3 - P. 273-285.

14. Jain A.K., Murty M.N., Flynn P.J. Data clustering: a review // ACM computing surveys (CSUR). - N.Y., 1999. - Т. 31, N 3. - P. 264-323.

15. Savoia A., Sen K. Measurement and evolution of state capacity: Exploring a lesser known aspect of governance. - Manchester, UK, 2012. - 28 p. - (ESID Working Paper; N 10).

16. Shawe-Taylor J., Cristianini N. Kernel methods for pattern analysis. - Cambridge: Cambridge univ. press, 2004. - Mode of access: http://read.pudn.com/downloads190/ebook/893343/Kernel_Methods_for_Pattern_Analysis/0521813972.pdf (accessed: 20.04.2019.)

17. Wang S. The Rise of the Regions: Fiscal Reform and the Decline of Central State Capacity in China // The Waning of the Communist State: Economic Origins of Political Decline in China and Hungary / Ed. by A. Walder. - Berkeley: Univ. of California press, 1995. - P. 87-114.

18. Xu R., Wunsch D.C. Survey of clustering algorithms // IEEE Transactions on Neural Networks. - N.Y., 2005. - Vol. 16, N 3. - P. 645-678. - DOI: https://doi.org/10.1109/TNN.2005.845141