A Multiagent Approach to Adaptive Continuous Analysis of Streaming Data in Complex Uncertain Environments


The data mining task of online unsupervised learning of streaming data continually arriving at the system in complex dynamic environments under conditions of uncertainty is an NP-hard optimization problem for general metric spaces and is computationally intractable for real-world problems of practical interest. The primary contribution of this work is a multi-agent method for continuous agglomerative hierarchical clustering of streaming data, and a knowledge-based selforganizing competitive multi-agent system for implementing it. The reported experimental results demonstrate the applicability and efficiency of the implemented adaptive multi-agent learning system for continuous online clustering of both synthetic datasets and datasets from the following real-world domains: the RoboCup Soccer competition, and gene expression datasets from a bioinformatics test bed.


Online Learning Gene Expression Dataset Streaming Data Cluster Agent Mining Agent 
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.


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  1. 1.
    Aggarwal, C.C. (ed.): Data Streams: Models and Algorithms. Advances in Database Systems. Springer, New York, NY, USA (2007)Google Scholar
  2. 2.
    Al-Shalalfa, M., Alhajj, R.: Application of double clustering to gene expression data for class prediction. In: AINA Workshops (1), pp. 733–738. IEEE Computer Society (2007)Google Scholar
  3. 3.
    Auroop R. Ganguly Joao Gama, O.A.O.M.M.G.R.R.V. (ed.): Knowledge Discovery from Sensor Data. CRC Press Inc - Taylor and Francis Ltd, New York, NY, USA (2008)Google Scholar
  4. 4.
    Bagherjeiran, A., Eick, C.F., Chen, C.S., Vilalta, R.: Adaptive clustering: Obtaining better clusters using feedback and past experience. IEEE International Conference on Data Mining 0, 565–568 (2005)CrossRefGoogle Scholar
  5. 5.
    Davidson, I., Ravi, S.S.: Agglomerative hierarchical clustering with constraints: Theoretical and empirical results. In: PKDD-05, LNCS, vol. 3721. Springer (2005)Google Scholar
  6. 6.
    Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)CrossRefMATHMathSciNetGoogle Scholar
  7. 7.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: A review. ACM Comput. Surv. 31(3), 264–323 (1999)CrossRefGoogle Scholar
  8. 8.
    Kiselev, I., Alhajj, R.: An adaptive multi-agent system for continuous learning of streaming data. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT’08) 2, 148–153 (2008)CrossRefGoogle Scholar
  9. 9.
    Kiselev, I., Alhajj, R.: Online dynamic optimization under conditions of uncertainty. In: A.P.S.D.R. Nicholas R. Jennings Alex Rogers (ed.) 1th International Workshop “Optimisation in Multi-Agent Systems” (OptMAS), pp. 52–59. AAMAS-08 (2008)Google Scholar
  10. 10.
    Kiselev, I., Alhajj, R.: Supplementary materials, software demonstration, and video recordings of the developed multi-agent learning system, described in this work. Website (2008). \footnotesize{\ttfamily{http://www.multiagent.org/mining}}
  11. 11.
    Kiselev, I., Glaschenko, A., Chevelev, A., Skobelev, P.: Towards an adaptive approach for distributed resource allocation in a multi-agent system for solving dynamic vehicle routing problems. In: AAAI-07, pp. 1874–1875 (2007)Google Scholar
  12. 12.
    Klusch, M., Lodi, S., Moro, G.: Issues of agent-based distributed data mining. In: AAMAS-03, pp. 1034–1035 (2003)Google Scholar
  13. 13.
    Likas, A.: A reinforcement learning approach to online clustering. Neural Computation 11(8), 1915–1932 (1999)CrossRefGoogle Scholar
  14. 14.
    MacKie-Mason, J.K., Wellman, M.P.: Handbook of Computational Economics, vol. Volume 2, chap. Chapter 28 Automated Markets and Trading Agents, pp. 1381–1431. Elsevier (2006)Google Scholar
  15. 15.
    Modi, P.J., Jung, H., Tambe, M., Shen, W.M., Kulkarni, S.: Dynamic distributed resource allocation: A distributed constraint satisfaction approach. In: ATAL: Revised Papers from the 8th Intern. Workshop on Intelligent Agents VIII, pp. 264–276. Springer-Verlag, UK (2002)Google Scholar
  16. 16.
    Modi, P.J., Shen, W.M., Tambe, M., Yokoo, M.: Adopt: asynchronous distributed constraint optimization with quality guarantees. Artif. Intell. 161(1–2), 149–180 (2005)CrossRefMATHMathSciNetGoogle Scholar
  17. 17.
    Nisan, N., Roughgarden, T., Tardos, E., Vazirani, V.V. (eds.): Algorithmic Game Theory. Cambridge University Press, New York, NY, USA (2007)Google Scholar
  18. 18.
    Rodrigues, P.P., Gama, J., Pedroso, J.P.: Hierarchical clustering of time-series data streams. IEEE Trans. Knowl. Data Eng. 20(5), 615–627 (2008)CrossRefGoogle Scholar
  19. 19.
    Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd international ed. edn. Prentice-Hall, Upper Saddle River, NJ, USA (2003)Google Scholar
  20. 20.
    Smith, R.G.: The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Trans. Computers 29(12), 1104–1113 (1980)Google Scholar
  21. 21.
    Symeonidis, A.L., Mitkas, P.A.: Agent Intelligence Through Data Mining. Multiagent Systems, Artificial Societies, and Simulated Organizations. Springer-Verlag New York (2005)Google Scholar
  22. 22.
    Theocharopoulou, C., Partsakoulakis, I., Vouros, G.A., Stergiou, K.: Overlay networks for task allocation and coordination in dynamic large-scale networks of cooperative agents. In: E.H. Durfee, M. Yokoo, M.N. Huhns, O. Shehory (eds.) AAMAS, p. 55. IFAAMASGoogle Scholar
  23. 23.
    Zhang, S., Zhang, C., Wu, X.: Knowledge Discovery in Multiple Databases. Springer-Verlag (2004)Google Scholar

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© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.The David R. Cheriton School of Computer ScienceUniversity of WaterlooWaterlooCanada
  2. 2.Department of Computer ScienceUniversity of CalgaryCalgaryCanada

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