Advertisement

Using Agent Based Modeling and Simulation for Data Mining

  • Emin Kugu
  • Levent Altay
  • Ozgur Koray Sahingoz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7664)

Abstract

In recent years, there is an exponential growth of information sources, especially with the increasing usage of Internet. Therefore, there is a growing need for automated tools for obtaining valuable information from these raw data in different data warehouses. Data Mining represents the process of extracting valuable and useful knowledge from large amounts of data. Generating appropriate abstractions from these distributed data warehouses is a challenging task for data mining tools. Data mining is a multidisciplinary research area and it includes database technology, neural networks, artificial intelligence and machine learning etc. It enables valuable information to the end users. However, if the system is newly set and it is in the cold start position with no or little processed data, this influences the system efficiency. There is an additional mechanism for producing realistic data. Agent Based Modeling and Simulation system is a powerful technology by using autonomous intelligent agents and usually can run in distributed environment. This paper emphasizes the approach of using Agent Based Modeling and Simulation for Distributed Data Mining technologies.

Keywords

Agent MAS Data Mining Agent Based Modeling and Simulation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Liao, H., Yang, L., Geng, X.: Application Research of New Distributed Data Mining Model Based on Intelligent Agent and Web Service in Digital Gas Fields. In: 2011 International Conference on Computational and Information Sciences (ICCIS), pp. 137–140 (2011)Google Scholar
  2. 2.
    Datta, S., Bhaduri, K., Giannella, C., Wolff, R., Kargupta, H.: Distributed Data Mining in Peer-to-Peer Networks. IEEE Internet Computing 10(4), 18–26 (2006)CrossRefGoogle Scholar
  3. 3.
    Longbing, C.: Introduction to Agent Mining Interaction and Integration. In: Data Mining and Multi-Agent Integration, Part 1, pp. 3–36 (2009)Google Scholar
  4. 4.
    Macal, C.M., North, M.J.: Introductory tutorial: Agent-based modeling and simulation. In: Proceedings of the 2011 Winter Simulation Conference (WSC), pp. 1451–1464 (2011)Google Scholar
  5. 5.
    Longbing, C.: Agent-mining interaction: theoretical challenges and prospects. Technical report (2006)Google Scholar
  6. 6.
    Better, M., Glover, F., Kochenberger, G., Wang, H.: Simulation Optimization: Applications in Risk Management. International Journal of Information Technology & Decision Making 7(4), 571–587 (2008)zbMATHCrossRefGoogle Scholar
  7. 7.
    Gorodetsky, V., Karsaev, O., Samoilov, V.: Multi-agent Technology for Distributed Data Mining and Classification. In: IAT, pp. 438–441. IEEE Computer Society (2003)Google Scholar
  8. 8.
    Klusch, M., Lodi, S., Moro, G.: Issues of agent-based distributed data mining. In: Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2003), pp. 1034–1035. ACM (2003)Google Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    JADE (Java Agent DEvelopment Framework), http://jade.tilab.com/
  11. 11.
    North, M.J., Collier, N.T., Vos, J.R.: Experiences Creating Three Implementations of the Repast Agent Modeling Toolkit. ACM Transactions on Modeling and Computer Simulation 16(1), 1–25 (2006)CrossRefGoogle Scholar
  12. 12.
    Remondino, M., Correndo, G.: Data Mining Applied to Agent Based Simulation. In: Proceedings 19th European Conference on Modeling and Simulation-ECMS (2005)Google Scholar
  13. 13.
    Baqueiro, O., Wang, Y.J., McBurney, P., Coenen, F.: Integrating Data Mining and Agent Based Modeling and Simulation. In: Perner, P. (ed.) ICDM 2009. LNCS, vol. 5633, pp. 220–231. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Hassan, S., Pavón, J., Antunes, L., Gilbert, N.: Injecting Data into Agent-Based Simulation. In: The Second World Congress on Social Simulation. Springer Series on Agent Based Social Systems (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Emin Kugu
    • 1
  • Levent Altay
    • 1
  • Ozgur Koray Sahingoz
    • 1
  1. 1.Computer Engineering DepartmentTurkish Air Force AcademyIstanbulTurkey

Personalised recommendations