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)


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.


Clustering Multi-agent system Self-optimization 


  1. 1.
    Chaimontree, S., Atkinson, K., Coenen, F.: A multi-agent based approach to clustering: harnessing the power of agents. In: Cao, L., Bazzan, A.L.C., Symeonidis, A.L., Gorodetsky, V.I., Weiss, G., Yu, P.S. (eds.) Agents and Data Mining Interaction. ADMI 2011. Lecture Notes in Computer Science, vol. 7103. Springer, Berlin, Heidelberg (2012)Google Scholar
  2. 2.
    Cluster Analysis. Last accessed 10 Jan 2019
  3. 3.
    Frank, A., Asuncion, A.: UCI machine learning repository. (2010)
  4. 4.
    Bailey, S., Grossman, R., Sivakumar, H., Turinsky, A.: Papyrus: a system for data min-ing over local and wide area clusters and super-clusters. In: IEEE Supercomputing (1999)Google Scholar
  5. 5.
    Giannella, C., Bhargava, R., Kargupta, H.: Multi-agent systems and distributed data mining. In: Klusch, M., Ossowski, S., Kashyap, V., Unland, R. (eds.) CIA 2004. LNCS (LNAI), vol. 3191, pp. 1–15. Springer, Heidelberg (2004)Google Scholar
  6. 6.
    Kiselev, I., Alhajj, R.: A self-organizing multi-agent system for online unsupervised learning in complex dynamic environments. In: Proceedings of the 23rd AAAI Conference on Artificial Intelligence, pp. 1808–1809. AAAI Press (2008)Google Scholar
  7. 7.
    Agogino, A., Tumer, K.: Efficient agent-based cluster ensembles. In: Proceedings of the 5th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2006, pp. 1079–1086. ACM, New York (2006)Google Scholar
  8. 8.
    Park, J.-E., Oh, K.-W.: Multi-agent systems for intelligent clustering. In: World Academy of Science, Engineering and Technology, vol. 11 (2007)Google Scholar
  9. 9.
    Reed, J.W., et al.: A multi-agent system for distributed cluster analysis (2004)Google Scholar
  10. 10.
    Ogston E., Overeinder B., van Steen M., Brazier F.: A method for decentralized clustering in large multi-agent systems. In: Proceeding AAMAS ‘03 Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 789–796, Melbourne, Australia (2003)Google Scholar
  11. 11.
    Xin, P., Sagan, H.: Digital image clustering algorithm based on multi-agent center optimization. J. Digit. Inf. Manag. 14(1), 8–14 (2016)Google Scholar
  12. 12.
    Klusch, M., Lodi, S., Moro, G.: Agent-based distributed data mining: the KDEC scheme. In: Proceedings of the Springer Lecture Notes in Computer Science, vol. 2586, pp. 104–122 (2003)CrossRefGoogle Scholar
  13. 13.
    Äyrämö, S., Kärkkäinen, T.: Introduction to partitioning-based clustering methods with a robust example. Reports of the Department of Mathematical Information Technology, Series C. In: Software and Computational Engineering, No. C. 1. University of Jyväskylä, Department of Mathematical Information Technology, Finland (2006)Google Scholar
  14. 14.
    Hyacinth, S.: Nwana: software agents: an overview. Knowl. Eng. Rev. 11(3), 205–244 (1996)CrossRefGoogle Scholar
  15. 15.
    Kadar, M., Tulbure, A.: An automated knowledge discovery framework with multi-agent systems—KDMAS. In: Proceedings of the International Conference on Engineering, Technology and Innovation (ICE/ITMC), pp. 1304–1309. IEEE, Madeira Island, Portugal, 978-1-5386-0774-9/17/©2017 (2017)Google Scholar
  16. 16.
    The University of Waikako, NZ. Last accessed 27 Dec 2018
  17. 17.
    From Pandas to Scikit-Learn. Last accessed 17 Feb 2019

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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