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Review

Adaptive Clustering through Multi-Agent Technology: Development and Perspectives

1
Institute of Automation and Information Technologies, Samara State Technical University, 443100 Samara, Russia
2
Department of Information Systems and Technologies, Samara National Research University, 443086 Samara, Russia
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(10), 1664; https://doi.org/10.3390/math8101664
Received: 13 September 2020 / Revised: 23 September 2020 / Accepted: 24 September 2020 / Published: 27 September 2020
(This article belongs to the Special Issue Machine Learning and Data Mining in Pattern Recognition)
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. View Full-Text
Keywords: multi-agent technology; adaptive clustering; resource planning and scheduling; if-then rules; logistics; schedule generation; pattern extraction; order consolidation; real-time multi-agent technology; adaptive clustering; resource planning and scheduling; if-then rules; logistics; schedule generation; pattern extraction; order consolidation; real-time
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MDPI and ACS Style

Grachev, S.; Skobelev, P.; Mayorov, I.; Simonova, E. Adaptive Clustering through Multi-Agent Technology: Development and Perspectives. Mathematics 2020, 8, 1664. https://doi.org/10.3390/math8101664

AMA Style

Grachev S, Skobelev P, Mayorov I, Simonova E. Adaptive Clustering through Multi-Agent Technology: Development and Perspectives. Mathematics. 2020; 8(10):1664. https://doi.org/10.3390/math8101664

Chicago/Turabian Style

Grachev, Sergey, Petr Skobelev, Igor Mayorov, and Elena Simonova. 2020. "Adaptive Clustering through Multi-Agent Technology: Development and Perspectives" Mathematics 8, no. 10: 1664. https://doi.org/10.3390/math8101664

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