Agent-Mining Interaction: An Emerging Area

  • Longbing Cao
  • Chao Luo
  • Chengqi Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4476)


In the past twenty years, agents (we mean autonomous agent and multi-agent systems) and data mining (also knowledge discovery) have emerged separately as two of most prominent, dynamic and exciting research areas. In recent years, an increasingly remarkable trend in both areas is the agent-mining interaction and integration. This is driven by not only researcher’s interests, but intrinsic challenges and requirements from both sides, as well as benefits and complementarity to both communities through agent-mining interaction. In this paper, we draw a high-level overview of the agent-mining interaction from the perspective of an emerging area in the scientific family. To promote it as a newly emergent scientific field, we summarize key driving forces, originality, major research directions and respective topics, and the progression of research groups, publications and activities of agent-mining interaction. Both theoretical and application-oriented aspects are addressed. The above investigation shows that the agent-mining interaction is attracting everincreasing attention from both agent and data mining communities. Some complicated challenges in either community may be effectively and efficiently tackled through agent-mining interaction. However, as a new open area, there are many issues waiting for research and development from theoretical, technological and practical perspectives.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aciar, S., et al.: Informed Recommender Agent: Utilizing Consumer Product Reviews through Text Mining. In: Proceedings of IADM2006, IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
  2. 2.
    Baik, S.W., Bala, J., Cho, J.: Performance Evaluation of an Agent Based Distributed Data Mining System. In: Kégl, B., Lapalme, G. (eds.) Canadian AI 2005. LNCS (LNAI), vol. 3501, pp. 25–32. Springer, Heidelberg (2005)Google Scholar
  3. 3.
    Cory, J., et al.: Proceedings of IADM2006 (Chaired by Cao, L., Zhang, Z., Zamoilov, V.). In: WI-IAT2006 Workshop Proceedings. IEEE Computer Society (2006)Google Scholar
  4. 4.
    Cao, L., et al.: Agent Services-Based Infrastructure for Online Assessment of Trading Strategies. In: Proceedings of IAT’04, pp. 345–349 (2004)Google Scholar
  5. 5.
    Cao, L.: Integration of agent and data mining. Technical report (25 June 2005),
  6. 6.
    Cao, L., Gorodetski, V.: AREA OVERVIEW–Agent & data mining interaction (ADMI). In: WI-IAT 2006 IADM Workshop panel discussion, Hongkong (2006)Google Scholar
  7. 7.
    Cao, L.: Agent-mining interaction: theoretical challenges and prospects. Technical report (2006)Google Scholar
  8. 8.
    Cao, L.: Agent-mining interaction: application challenges and prospects. Technical report (2006)Google Scholar
  9. 9.
    Cao, L.: Mutual issues in agent-mining interaction. Technical report (2006)Google Scholar
  10. 10.
    Brazdil, P., Muggleton, S.: Learning to Relate Terms in a Multiple Agent Environment. In: Kodratoff, Y. (ed.) EWSL 1991. LNCS, vol. 482, Springer, Heidelberg (1991)Google Scholar
  11. 11.
    Davies, W.: ANIMALS: A Distributed, Heterogeneous Multi-Agent Learning System. MSc Thesis, University of Aberdeen (1993)Google Scholar
  12. 12.
    Davies, W.: Agent-Based Data-Mining (1994)Google Scholar
  13. 13.
    Edwards, P., Davies, W.: A Heterogeneous Multi-Agent Learning System. In: Deen, S.M. (ed.) Proceedings of the Special Interest Group on Cooperating Knowledge Based Systems, University of Keele, pp. 163–184 (1993)Google Scholar
  14. 14.
    Gorodetsky, V., Liu, J., Skormin, V.A.: Autonomous Intelligent Systems: Agents and Data Mining book. In: Gorodetsky, V., Liu, J., Skormin, V.A. (eds.) AIS-ADM 2005. LNCS (LNAI), vol. 3505, Springer, Heidelberg (2005)Google Scholar
  15. 15.
    Gorodetsky, V., Karsaeyv, O., Samoilov, V.: Multi-agent technology for distributed data mining and classification Intelligent Agent Technology. In: IAT 2003, vol. 2003, pp. 438–441 (2003)Google Scholar
  16. 16.
    Gorodetsky, V., Karsaev, O., Samoilov, V.: Infrastructural Issues for Agent-Based Distributed Learning. In: Proceedings of IADM2006, IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
  17. 17.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2006)Google Scholar
  18. 18.
    Kaya, M., Alhajj, R.: A novel approach to multiagent reinforcement learning: utilizing OLAP mining in the learning process. IEEE Transactions on Systems, Man and Cybernetics, Part C 35(4), 582–590 (2005)CrossRefGoogle Scholar
  19. 19.
    Kaya, M., Alhajj, R.: Fuzzy OLAP association rules mining-based modular reinforcement learning approach for multiagent systems. IEEE Transactions on Systems, Man and Cybernetics, Part B 35(2), 326–338 (2005)CrossRefGoogle Scholar
  20. 20.
    Klusch, M., Lodi, S., Gianluca, M.: The role of agents in distributed data mining: issues and benefits. In: Intelligent Agent Technology, pp. 211–217 (2003)Google Scholar
  21. 21.
    Klusch, M., Lodi, S., Moro, G.: Agent-Based Distributed Data Mining: The KDEC Scheme. In: Klusch, M., et al. (eds.) Intelligent Information Agents. LNCS (LNAI), vol. 2586, Springer, Heidelberg (2003)Google Scholar
  22. 22.
    Klusch, M., Lodi, S., Moro, G.: Issues of agent-based distributed data mining. In: Proceedings of AAMAS, ACM Press, New York (2003)Google Scholar
  23. 23.
    Letia, I.A., et al.: First Experiments for Mining Sequential Patterns on Distributed Sites with Multi-agents. In: Leung, K.-S., Chan, L., Meng, H. (eds.) IDEAL 2000. LNCS, vol. 1983, pp. 187–192. Springer, Heidelberg (2000)Google Scholar
  24. 24.
    Liu, J., You, J.: Smart shopper: an agent-based web-mining approach to Internet shopping. IEEE Transactions on Fuzzy Systems 11(2) (2003)Google Scholar
  25. 25.
    Mitkas, P.: Knowledge Discovery for Training Intelligent Agents: Methodology, Tools and Applications. In: Gorodetsky, V., Liu, J., Skormin, V.A. (eds.) AIS-ADM 2005. LNCS (LNAI), vol. 3505, Springer, Heidelberg (2005)Google Scholar
  26. 26.
    Sterling, L., Lu, H., Wyatt, A.: Knowledge Discovery in SportsFinder: An Agent to Extract Sports Results from the Web. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol. 1574, pp. 469–473. Springer, Heidelberg (1999)Google Scholar
  27. 27.
    Mohammadian, M.: Intelligent Agents for Data Mining and Information Retrieval. Idea Group Publishing, Hershey (2004)Google Scholar
  28. 28.
    Ong, K., et al.: Agents and stream data mining: a new perspective. IEEE Intelligent Systems 20(3), 60–67 (2005)CrossRefGoogle Scholar
  29. 29.
    Rea, S.: Building Intelligent.NET Applications: Agents, Data Mining, Rule-Based Systems, and Speech Processing. Addison-Wesley, Reading (2004)Google Scholar
  30. 30.
    Sian, S.: Extending Learning to Multiple Agents: Issues and a Model for Multi-Agent Machine Learning (MA-ML). In: Kodratoff, Y. (ed.) EWSL 1991. LNCS, vol. 482, pp. 458–472. Springer, Heidelberg (1991)CrossRefGoogle Scholar
  31. 31.
    Symeonidis, A., Mitkas, P.: Agent Intelligence Through Data Mining. Springer, Heidelberg (2006)Google Scholar
  32. 32.
    Wooldridge, M.: An Introduction to MultiAgent Systems. Wiley, Chichester (2002)Google Scholar
  33. 33.
    Zhang, C., Zhang, Z., Cao, L.: Agents and Data Mining: Mutual Enhancement by Integration. In: Gorodetsky, V., Liu, J., Skormin, V.A. (eds.) AIS-ADM 2005. LNCS (LNAI), vol. 3505, pp. 50–61. Springer, Heidelberg (2005)Google Scholar
  34. 34.
    Zhang, C., Zhang, Z.: Agent-Based Hybrid Intelligent System for Data Mining. In: Zhang, Z., Zhang, C. (eds.) Agent-Based Hybrid Intelligent Systems. LNCS (LNAI), vol. 2938, pp. 127–142. Springer, Heidelberg (2004)Google Scholar
  35. 35.
    Zhong, N., Liu, J., Sun, R.: Intelligent agents and data mining for cognitive systems? Cognitive Systems Research 5(3), 169–170 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Longbing Cao
    • 1
  • Chao Luo
    • 1
  • Chengqi Zhang
    • 1
  1. 1.Faculty of Information Technology, University of Technology, SydneyAustralia

Personalised recommendations