A Multiagent, Multiobjective Clustering Algorithm

  • Daniela S. SantosEmail author
  • Denise de Oliveira
  • Ana L. C. Bazzan


This chapter presents MACC, a multi ant colony and multiobjective clustering algorithm that can handle distributed data, a typical necessity in scenarios involving many agents. This approach is based on independent ant colonies, each one trying to optimize one particular feature objective. The multiobjective clustering process is performed by combining the results of all colonies. Experimental evaluation shows that MACC is able to find better results than the case where colonies optimize a single objective separately.


Multiagent System Social Insect Swarm Intelligence Cluster Ensemble Pheromone Concentration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cao, L., Luo, C., Zhang, C.: Agent-mining interaction: An emerging area. In: AIS-ADM. Springer (2007)Google Scholar
  2. 2.
    Santos, C.T., Bazzan, A.L.C.: Integrating knowledge through cooperative negotiation – a case study in bioinformatics. In Gorodetsky, V., Liu, J., Skormin, V.A., eds.: Proceedings of the International Workshop on Autonomous Intelligent Systems: Agents and Data Mining. Number 3505 in Lecture Notes in Artificial Intelligence, Springer-Verlag (2005) 277–288Google Scholar
  3. 3.
    Faceli, K., Carvalho, A.C.P.L.F., Souto, M.C.P.: Multi-objective clustering ensemble. In: Proceedings of the Sixth International Conference on Hybrid Intelligent Systems (HIS 06), Washington, DC, USA, IEEE Computer Society (2006) 51Google Scholar
  4. 4.
    Kao, Y., Cheng, K.: An ACO-based clustering algorithm. In: Proceedings of the Fifth International Workshop on Ant Colony. Optimization and Swarm Intelligence - ANTS 2006. Volume 4150 of Lecture Notes in Computer Science., Brussels, Belgium, Springer (2006) 340–347Google Scholar
  5. 5.
    Lumer, E.D., Faieta, B.: Diversity and adaptation in populations of clustering ants. In: Proceedings of the third international conference on Simulation of adaptive behavior: from animals to animats, Cambridge, MA, USA, MIT Press (1994) 501–508Google Scholar
  6. 6.
    Shelokar, P.S., Jayaraman, V. K., Kulkarni, B.D.: An ant colony approach for clustering. Analytica Chimica Acta 509 (2004) 187–195CrossRefGoogle Scholar
  7. 7.
    Yang, Y., Kamel, M.: Clustering ensemble using swarm intelligence. In: Proceedings of the Swarm Intelligence Symposium (SIS 03), Indianapolis, USA (2003) 65–71Google Scholar
  8. 8.
    Yang, Y., Kamel, M.: An aggregated clustering approach using multi-ant colonies algorithms. Pattern Recongnition 39 (2006) 1278–1289CrossRefzbMATHGoogle Scholar
  9. 9.
    Handl, J., Konwles, J.: Exploiting the trade-off - the benefits of multiple objectives in data clustering. In: Proceedings of the Third International Conference on Evolutionary Multi-Criterion Optimization (EMO 2005), Springer Verlag (2005) 547–560Google Scholar
  10. 10.
    Camazine, S., Deneubourg, J.D., Franks, N.R., Sneyd, J., Theraulaz, G., Bonabeau, E.: Self-Organization in Biological Systems. Princeton University Press, Princeton, N.J. (2003)zbMATHGoogle Scholar
  11. 11.
    Gordon, D.: The organization of work in social insect colonies. Nature 380 (1996) 121–124CrossRefGoogle Scholar
  12. 12.
    Bonabeau, E., Theraulaz, G., Dorigo, M.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York, USA (1999)zbMATHGoogle Scholar
  13. 13.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics 26 (1996) 29–41CrossRefGoogle Scholar
  14. 14.
    Strehl, A., Ghosh, J.: Cluster ensembles: a knowledge reuse framework for combining partitionings. In: Proceedings of the Eighteenth National Conference Intelligence, Menlo Park, CA, USA, American Association for Artificial Intelligence (2002) 93–98Google Scholar
  15. 15.
    Deneubourg, J.L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chrétien, L.: The dynamics of collective sorting: Robot-like ant and ant-like robot. In: Proceedings of the First Conference on Simulation of Adaptive Behavior: From Animals to Animats, Canbridge, MA, USA, MIT Press (1991) 356–363Google Scholar
  16. 16.
    Agogino, A., Tumer, K.: Efficient agent-based cluster ensembles. In Stone, P., Weiss, G., eds.: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems, AAMAS ’06, New York, NY, USA, ACM (2006) 1079–1086CrossRefGoogle Scholar
  17. 17.
    Ertöz, L., Steinbach, M., Kumar, V.: A new shared nearest neighbor clustering algorithm and its applications. In: Proceedings of the International Conference on Data Mining (2nd SIAM). Volume 4., VA, IEEE Press (2002) 2642–2647Google Scholar
  18. 18.
    Asuncion, A., Newman, D.J.: UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences (2007)∼mlearn/MLRepository.html.

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Daniela S. Santos
    • 1
    Email author
  • Denise de Oliveira
    • 1
  • Ana L. C. Bazzan
    • 1
  1. 1.Instituto de Informática, UFRGSRSBrazil

Personalised recommendations