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.
Highlights
The known clustering task consists of categorizing a given matters collection according to its inner similarity, such that items belonging to the same group are more alike to each other in comparison to ones located in additional sets
We review and generalize our previously developed principles, methods, and solutions for real-time adaptive clustering based on multi-agent technology, the main principles and approaches of which are set out, for example, by the authors of [20]
TheResults process of validation of the adaptive clustering solution was organized with the use of the manually selectedof documents, in several groupssolution formed was by the semanticwith similarity
Summary
The known clustering task consists of categorizing a given matters collection according to its inner similarity, such that items belonging to the same group (cluster) are more alike to each other in comparison to ones located in additional sets. Such a problem is typically resolved with a predefined number of groups [1,2,3,4,5,6,7,8]. The main limitation of many current data mining methods and algorithms is the need to suggest a hypothesis about data configuration in advance that is frequently impossible in the online mode
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