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A Multi-agent System with Self-optimization for Automated Clustering (MASAC)

  • Manuella KadarEmail author
  • Maria Viorela Muntean
  • Tudor Csabai
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 148)

Abstract

Multi-agent Systems (MAS) offer an alternative to handling large quantities of data with the added advantage that control is not centralized, and consequently, such systems are endowed with robustness and versatility. This paper describes a Multi-agent System for Automated Clustering with self-optimization (MASAC). The framework comprises six categories of agents: information updater agent, document uploader agent, parser agent, convertor agent, clustering agent, and subset extractor agent. A novelty and feature of MASAC is that it supports self-optimization allowing for the enhancement of the initial clusters configuration in real time, and not only after running a cluster validation agent, as in other MAS presented in the literature.

Keywords

Clustering Multi-agent system Self-optimization 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Manuella Kadar
    • 1
    Email author
  • Maria Viorela Muntean
    • 1
  • Tudor Csabai
    • 2
  1. 1.Decembrie 1918 UniversityAlba IuliaRomania
  2. 2.ContinentalSibiuRomania

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