Adaptive Clustering through Multi-Agent Technology: Development and Perspectives

Sergey Grachev, Petr Skobelev, Igor Mayorov and Elena Simonova
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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

Mathematics, 2020, vol. 8, issue 10, 1-17

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.

Keywords: multi-agent technology; adaptive clustering; resource planning and scheduling; if-then rules; logistics; schedule generation; pattern extraction; order consolidation; real-time (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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