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Adaptive Clustering through Multi-Agent Technology: Development and Perspectives

Author

Listed:
  • Sergey Grachev

    (Institute of Automation and Information Technologies, Samara State Technical University, 443100 Samara, Russia)

  • Petr Skobelev

    (Institute of Automation and Information Technologies, Samara State Technical University, 443100 Samara, Russia)

  • Igor Mayorov

    (Institute of Automation and Information Technologies, Samara State Technical University, 443100 Samara, Russia)

  • Elena Simonova

    (Department of Information Systems and Technologies, Samara National Research University, 443086 Samara, Russia)

Abstract

The paper is devoted to an overview of multi-agent principles, methods, and technologies intended to adaptive real-time data clustering. The proposed methods provide new principles of self-organization of records and clusters, represented by software agents, making it possible to increase the adaptability of different clustering processes significantly. The paper also presents a comparative review of the methods and results recently developed in this area and their industrial applications. An ability of self-organization of items and clusters suggests a new perspective to form groups in a bottom-up online fashion together with continuous adaption previously obtained decisions. Multi-agent technology allows implementing this methodology in a parallel and asynchronous multi-thread manner, providing highly flexible, scalable, and reliable solutions. Industrial applications of the intended for solving too complex engineering problems are discussed together with several practical examples of data clustering in manufacturing applications, such as the pre-analysis of customer datasets in the sales process, pattern discovery, and ongoing forecasting and consolidation of orders and resources in logistics, clustering semantic networks in insurance document processing. Future research is outlined in the areas such as capturing the semantics of problem domains and guided self-organization on the virtual market.

Suggested Citation

  • Sergey Grachev & Petr Skobelev & Igor Mayorov & Elena Simonova, 2020. "Adaptive Clustering through Multi-Agent Technology: Development and Perspectives," Mathematics, MDPI, vol. 8(10), pages 1-17, September.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:10:p:1664-:d:420277
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    References listed on IDEAS

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    1. repec:cup:cbooks:9780511771576 is not listed on IDEAS
    2. Easley,David & Kleinberg,Jon, 2010. "Networks, Crowds, and Markets," Cambridge Books, Cambridge University Press, number 9780521195331, September.
    3. Z. Volkovich & D. Toledano-Kitai & G.-W. Weber, 2013. "Self-learning K-means clustering: a global optimization approach," Journal of Global Optimization, Springer, vol. 56(2), pages 219-232, June.
    4. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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    Cited by:

    1. Min Chen & Ashutosh Sharma & Jyoti Bhola & Tien V. T. Nguyen & Chinh V. Truong, 2022. "Multi-agent task planning and resource apportionment in a smart grid," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 444-455, March.

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