Abstract
This paper commences with a discussion of the advantages that Multi-Agent Systems (MAS) can bring to the domain of Knowledge Discovery in Data (KDD), and presents a rational for Agent-Enriched Data Mining (AEDM). A particular challenge of any generic, general purpose, AEDM system is the extensive scope of KDD. To address this challenge the authors suggest that any truly generic AEDM must be readily extendable and propose EMADS, The Extendable Multi-Agent Data mining System. A complete overview of the architecture and agent interaction models of EMADS is presented. The system’s operation is described and illustrated in terms of two KDD scenarios: meta association rule mining and classifier generation. In conclusion the authors suggest that EMADS provides a sound foundation for both KDD research and application based AEDM.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aggarwal, C., Yu, P.: A Condensation Approach to Privacy Preserving DataMining. In: Bertino, E., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K., Ferrari, E. (eds.) EDBT 2004. LNCS, vol. 2992, pp. 183–199. Springer, Heidelberg (2004)
Agrawal, R., Mehta, M., Shafer, J., Srikant, R., Arning, A., Bollinger, T.: The Quest Data Mining System. In: Proceedings 2nd Int. Conf. Knowledge Discovery and Data Mining, KDD (1996)
Albashiri, K., Coenen, F., Sanderson, R., Leng, P.: Frequent Set Meta Mining: Towards Multi-Agent Data Mining. In: Bramer, M., Coenen, F.P., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIII, pp. 139–151. Springer, London (2007)
Albashiri, K., Coenen, F., Leng, P.: Agent Based Frequent Set Meta Mining: Introducing EMADS. In: Artificial Intelligence in Theory and Practice II, Proceedings IFIP, pp. 23–32. Springer, Heidelberg (2007)
Baazaoui, H., Faiz, S., Ben Hamed, R., Ben Ghezala, H.: A Framework for data mining based multi-agent: an application to spatial data. In: 3rd World Enformatika Conference, WEC 2005, Avril, Istanbul (2005)
Bailey, S., Grossman, R., Sivakumar, H., Turinsky, A.: Papyrus: a system for data mining over local and wide area clusters and super-clusters. In: Proceedings Conference on Supercomputing, p. 63. ACM Press, New York (1999)
Bellifemine, F., Poggi, A., Rimassi, G.: JADE: A FIPA-Compliant agent framework. In: Proceedings Practical Applications of Intelligent Agents and Multi-Agents, pp. 97–108 (1999), http://sharon.cselt.it/projects/jade
Blake, C., Merz, C.: UCI Repository of machine learning databases. University of California, Department of Information and Computer Science, Irvine, CA (1998), http://www.ics.uci.edu/mlearn/MLRepository.html
Bose, R., Sugumaran, V.: IDM: An Intelligent Software Agent Based DataMining Environment. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, pp. 2888–2893. IEEE Press, San Diego (1998)
Bota, J., Gmez-Skarmeta, A., Valds, M., Padilla, A.: Metala: A meta-learning architecture. Fuzzy Days, 688–698 (2001)
Cao, L., Zhang, C.: F-Trade: Agent-mining symbiont for financial services. In: AAMAS, pp. 1363–1364 (2007)
Coenen, F., Leng, P., Goulbourne, G.: Tree Structures for Mining Association Rules. Journal of Data Mining and KDD 8, 25–51 (2004)
Coenen, F., Leng, P., Zhang, L.: Threshold Tuning for Improved Classification Association Rule Mining. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS, vol. 3518, pp. 216–225. Springer, Heidelberg (2005)
Foundation for Intelligent Physical Agents, FIPA 2002 Specification. Geneva, Switzerland (2002), http://www.fipa.org/specifications/index.html
Giuseppe, D., Giancarlo, F.: A customizable multi-agent system for distributed data mining. In: Proc. of the 2007 ACM symp. on applied computing, pp. 42–47 (2007)
Gorodetsky, V., Karsaeyv, O., Samoilov, V.: Multi-agent technology for distributed data mining and classification. In: Proceedings Int. Conf. on Intelligent Agent Technology (IAT 2003), IEEE/WIC, pp. 438–441 (2003)
Grossman, R., Turinsky, A.: A framework for finding distributed data mining strategies that are intermediate between centralized strategies and in-place strategies. In: KDD Workshop on Distributed Data Mining (2000)
Kargupta, H., Byung-Hoon, et al.: Collective Data Mining: A New Perspective Toward Distributed Data Mining. In: Advances in Distributed and Parallel Knowledge Discovery. MIT/AAAI Press (1999)
Kargupta, H., Hamzaoglu, I., Stafford, B.: Scalable, Distributed Data Mining Using an Agent Based Architecture. In: Proceedings of Knowledge Discovery and Data Mining, pp. 211–214. AAAI Press, Menlo Park (1997)
Klusch, M., Lodi, G.: Agent-based Distributed Data Mining: The KDEC Scheme. In: Klusch, M., Bergamaschi, S., Edwards, P., Petta, P. (eds.) Intelligent Information Agents. LNCS, vol. 2586, pp. 104–122. Springer, Heidelberg (2003)
Liu, B., Hsu, W., Ma, Y.: Integrating Classification and Assocoiation Rule Mining. In: Proceedings KDD 1998, New York, August 27-31, pp. 80–86. AAAI, Menlo Park (1998)
Li, W., Han, J., Pei, J.: CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules. In: Proceedings ICDM, pp. 369–376 (2001)
Luo, P., Huang, R., He, Q., Lin, F., Shi, Z.: Execution engine of meta-learning system for kdd in multi-agent environment. Technical report, Institute of Computing Technology. Chinese Academy of Sciences (2005)
METAL Project. Esprit Project METAL (2002), http://www.metal-kdd.org
Peng, S., Mukhopadhyay, S., Raje, R., Palakal, M., Mostafa, J.: A Comparison Between Single-agent and Multi-agent Classification of Documents. In: Proceedings 15th Intern. PD Processing Symposium, pp. 935–944 (2001)
Prodromides, A., Chan, P., Stolfo, S.: Meta-Learning in Distributed Data Mining Systems: Issues and Approaches, pp. 81–114. AAAI Press/The MIT Press (2000)
Quinlan, J.R., Cameron-Jones, R.M.: FOIL: A Midterm Report. In: Brazdil, P.B. (ed.) ECML 1993. LNCS, vol. 667, pp. 3–20. Springer, Heidelberg (1993)
Shi, Z., Zhang, H., Cheng, Y., Jiang, Y., Sheng, Q., Zhao, Z.: Mage: An agent-oriented programming environment. In: Proceedings of the IEEE International Conference on Cognitive Informatics, pp. 250–257 (2004)
Stolfo, S., Prodromidis, A.L., Tselepis, S., Lee, W.: JAM: Java Agents for Meta-Learning over Distributed Databases. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining, pp. 74–81 (1997)
Vilalta, R., Christophe, G., Giraud-Carrier, B.P., Soares, C.: Using Meta-Learning to Support Data Mining. IJCSA 1(1), 31–45 (2004)
Yin, X., Han, J.: CPAR: Classification based on Predictive Association Rules. In: Proc. SIAM Int. Conf. on Data Mining (SDM 2003), SF, CA, pp. 331–335 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Albashiri, K.A., Coenen, F. (2009). Agent-Enriched Data Mining Using an Extendable Framework. In: Cao, L., Gorodetsky, V., Liu, J., Weiss, G., Yu, P.S. (eds) Agents and Data Mining Interaction. ADMI 2009. Lecture Notes in Computer Science(), vol 5680. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03603-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-03603-3_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03602-6
Online ISBN: 978-3-642-03603-3
eBook Packages: Computer ScienceComputer Science (R0)