Empirical testing of institutional matrices theory by data mining

Kirilyuk I.L., Volynsky A.I., Kruglova M.S., Kuznetsova A.V., Rubinstein A.A., Sen'ko O.V.
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The paper has a goal to identify a set of parameters of the environment and infrastructure with the most significant impact on institutional-matrices that dominate in different countries. Parameters of environmental conditions includes raw statistical indices, which were directly derived from the databases of open access, as well as complex integral indicators that were by method of principal components. Efficiency of discussed parameters in task of dominant institutional matrices type recognition (X or Y type) was evaluated by a number of methods based on machine learning. It was revealed that greatest informational content is associated with parameters characterizing risk of natural disasters, level of urbanization and the development of transport infrastructure, the monthly averages and seasonal variations of temperature and precipitation.

Keywords: institutional matrices theory, machine learning
Citation in English: Kirilyuk I.L., Volynsky A.I., Kruglova M.S., Kuznetsova A.V., Rubinstein A.A., Sen'ko O.V. Empirical testing of institutional matrices theory by data mining // Computer Research and Modeling, 2015, vol. 7, no. 4, pp. 923-939
Citation in English: Kirilyuk I.L., Volynsky A.I., Kruglova M.S., Kuznetsova A.V., Rubinstein A.A., Sen'ko O.V. Empirical testing of institutional matrices theory by data mining // Computer Research and Modeling, 2015, vol. 7, no. 4, pp. 923-939
DOI: 10.20537/2076-7633-2015-7-4-923-939
URL: http://crm-en.ics.org.ru/journal/article/2349/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.
According to Crossref, this article is cited by:
  • I. L. Kirilyuk, A. V. Kuznetsova, O. V. Sen’ko, A. M. Morozov. Method for detecting significant patterns in panel data analysis. // Pattern Recognition and Image Analysis. — 2017. — V. 27, no. 1. — P. 94. DOI: 10.1134/S1054661817010072
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