Abstract
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.
This work is sponsored by Australian Research Council Discovery Grant (DP0773412, LP0775041, DP0667060, DP0449535), and UTS internal grants.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
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)
Cory, J., et al.: Proceedings of IADM2006 (Chaired by Cao, L., Zhang, Z., Zamoilov, V.). In: WI-IAT2006 Workshop Proceedings. IEEE Computer Society (2006)
Cao, L., et al.: Agent Services-Based Infrastructure for Online Assessment of Trading Strategies. In: Proceedings of IAT’04, pp. 345–349 (2004)
Cao, L.: Integration of agent and data mining. Technical report (25 June 2005), http://www-staff.it.uts.edu.au/~lbcao/publication/publications.htm
Cao, L., Gorodetski, V.: AREA OVERVIEW–Agent & data mining interaction (ADMI). In: WI-IAT 2006 IADM Workshop panel discussion, Hongkong (2006)
Cao, L.: Agent-mining interaction: theoretical challenges and prospects. Technical report (2006)
Cao, L.: Agent-mining interaction: application challenges and prospects. Technical report (2006)
Cao, L.: Mutual issues in agent-mining interaction. Technical report (2006)
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)
Davies, W.: ANIMALS: A Distributed, Heterogeneous Multi-Agent Learning System. MSc Thesis, University of Aberdeen (1993)
Davies, W.: Agent-Based Data-Mining (1994)
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)
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)
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)
Gorodetsky, V., Karsaev, O., Samoilov, V.: Infrastructural Issues for Agent-Based Distributed Learning. In: Proceedings of IADM2006, IEEE Computer Society Press, Los Alamitos (2006)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2006)
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)
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)
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)
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)
Klusch, M., Lodi, S., Moro, G.: Issues of agent-based distributed data mining. In: Proceedings of AAMAS, ACM Press, New York (2003)
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)
Liu, J., You, J.: Smart shopper: an agent-based web-mining approach to Internet shopping. IEEE Transactions on Fuzzy Systems 11(2) (2003)
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)
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)
Mohammadian, M.: Intelligent Agents for Data Mining and Information Retrieval. Idea Group Publishing, Hershey (2004)
Ong, K., et al.: Agents and stream data mining: a new perspective. IEEE Intelligent Systems 20(3), 60–67 (2005)
Rea, S.: Building Intelligent.NET Applications: Agents, Data Mining, Rule-Based Systems, and Speech Processing. Addison-Wesley, Reading (2004)
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)
Symeonidis, A., Mitkas, P.: Agent Intelligence Through Data Mining. Springer, Heidelberg (2006)
Wooldridge, M.: An Introduction to MultiAgent Systems. Wiley, Chichester (2002)
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)
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)
Zhong, N., Liu, J., Sun, R.: Intelligent agents and data mining for cognitive systems? Cognitive Systems Research 5(3), 169–170 (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Cao, L., Luo, C., Zhang, C. (2007). Agent-Mining Interaction: An Emerging Area. In: Gorodetsky, V., Zhang, C., Skormin, V.A., Cao, L. (eds) Autonomous Intelligent Systems: Multi-Agents and Data Mining. AIS-ADM 2007. Lecture Notes in Computer Science(), vol 4476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72839-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-540-72839-9_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72838-2
Online ISBN: 978-3-540-72839-9
eBook Packages: Computer ScienceComputer Science (R0)