Abstract
The term “pattern” refers to a combination of values of some features such that objects with these feature values significantly differ from other objects. This concept is a useful tool for the analysis of behavior of objects in both statics and dynamics. If the panel data describing the functioning of objects in time is available, we can analyze pattern changing behavior of the objects and identify either well adapted to the environment objects or objects with unusual and alarming behavior.In this paper we apply static and dynamic pattern analysis to the analysis of innovative development of the Russian regions in the long run and obtain a classification of regions by the similarity of the internal structure of these indicators and groups of regions carrying out similar strategies.
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Acknowledgements
This work is a part of a project of data analysis of science, education, and innovative activity performed by National Research University Higher School of Economics under the state contract No. 07.514.11.4144 “Development of an experimental sample of statistical analysis of science, education, and innovation software using advanced techniques: pattern analysis and data ontological modeling” with Ministry of Education and Science, code 2012-1.4-07-514-0041.
Authors express their sincere gratitude to the Laboratory of Decision Choice and Analysis NRU HSE (F. Aleskerov, L. Egorova, A. Myachin) and Laboratory of Algorithms and Technologies for Network Analysis NRU HSE, Russian Federation Government Grant N. 11.G34.31.0057 (L. Egorova) for partial financial support. The study was undertaken in the framework of the Program of Fundamental Studies of the Higher School of Economics in 2012–2013.
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Aleskerov, F., Egorova, L., Gokhberg, L., Myachin, A., Sagieva, G. (2014). A Method of Static and Dynamic Pattern Analysis of Innovative Development of Russian Regions in the Long Run. In: Batsyn, M., Kalyagin, V., Pardalos, P. (eds) Models, Algorithms and Technologies for Network Analysis. Springer Proceedings in Mathematics & Statistics, vol 104. Springer, Cham. https://doi.org/10.1007/978-3-319-09758-9_1
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