CLUSTERING METHODS IN LARGE-SCALE SYSTEMS

Denis V. Gadasin,
Moscow Technical University of Communications and Informatics, Moscow, Russia, dengadiplom@mail.ru

Andrey V. Shvedov,
Moscow Technical University of Communications and Informatics, Moscow, Russia, a.v.shvedov@mtuci.ru

Alyona A. Yudina,
Moscow Technical University of Communications and Informatics, Moscow, Russia, alenka5yudina@mail.ru

DOI: 10.36724/2664-066X-2020-6-5-21-24

SYNCHROINFO JOURNAL. Volume 6, Number 5 (2020). P. 21-24.

Abstract

Interactions between people, groups, organizations, and biological cells have a relationship character that can be represented as a network. The system properties of such networks, regardless of their physical nature, but clearly determining the performance of networks, create the totality of the real world. Complex networks – are naturally existing networks (graphs) that have complex topological properties. The researchers who participate and also make discoveries in this field come from various Sciences such as mathematics, computer science, physics, sociology, and engineering. Therefore, the results of research carry both theoretical knowledge and practical applications in these Sciences. This paper discusses the definition of complex networks. The main characteristics of complex networks, such as clustering and congestion, are considered. A popular social network is considered as a complex network. The calculation of nodes and links of the considered social network is made. The main types of AI development and training are highlighted.

Keywords: complex networks, network implementation, technology, node, communication, network, artificial intelligence, algorithm, characteristics, clustering, workload, training, algorithm, analysis, solution, problem.

References

1. D.V. Gadasin, A.V. Shvedov and Y. S. Litvin, “Paradigm of Inheritance in Large-Scale Systems,”2019 Systems of Signals Generating and Processing in the Field of on Board Communications, Moscow, Russia, 2019, pp. 1-5, doi: 10.1109/SOSG.2019.8706804.

2. V.A. Dokuchaev, V.V. Maklachkova, D.V. Makarova and L.V. Volkova, “Analysis of Data Risk Management Methods for Personal Data Information Systems,”2020 Systems of Signals Generating and Processing in the Field of on Board Communications, Moscow, Russia, 2020, pp. 1-5, doi: 10.1109/IEEECONF48371.2020.9078547.

3. A.T. Terekhin, E.V. Budilova, M.P. Karpenko, L.M. Kachalova, E.V. Chmyhova. Lyapunov function as a tool for studying cognitive and regulatory processes of the body. Computer research and modeling-2009 Vol. 1 No. 4. P. 449-456.

4. R.V. Dushkin, D.A. Movchan. Artificial intelligence. Moscow: DMK-Press publishing House, 2019. 160 p.

5. F.V. Grechnikov, V.R. Kargin. Fundamentals of scientific research: textbook. Samara: SSAU Publishing house, 2015. 111 p.