Abstract
In this paper we describe EMADS, an Extendible Multi-Agent Data mining System. The EMADS vision is that of a community of data mining agents, contributed by many individuals, interacting under decentralised control to address data mining requests. EMADS is seen both as an end user application and a research tool. This paper details the EMADS vision, the associated conceptual framework and the current implementation. Although EMADS may be applied to many data mining tasks; the study described here, for the sake of brevity, concentrates on agent based data classification. A full description of EMADS is presented.
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.
References
Wooldridge, M. (2003). An Introduction to Multi-Agent Systems. John Wiley and Sons (Chichester, England).
Kargupta, H., Hamzaoglu, I. and Stafford B. (1997). Scalable, Distributed Data Mining Using an Agent Based Architecture. Proceedings of Knowledge Discovery and Data Mining, AAAI Press, 211–214.
Gorodetsky, V., Karsaeyv, O., Samoilov, V. (2003). Multi-agent technology for distributed data mining and classification. Proc. Int. Conf. on Intelligent Agent Technology (IAT 2003), IEEEAVIC, pp438–441.
Peng, S., Mukhopadhyay, S., Raje, R., Palakal, M. and Mostafa, J. (2001). A Comparison Between Single-agent and Multi-agent Classification of Documents. Proc. 15th International Parallel and Distributed Processing Symposium. pp935–944.
Stolfo, S., Prodromidis, A. L., Tselepis, S. and Lee, W. (1997). JAM: Java Agents for Meta-Learning over Distributed Databases. Proceedings of the International Conference on Knowledge Discovery and Data Mining, pp. 74–81.
Kargupta, H., Byung-Hoon, et al. (1999). Collective Data Mining: A New Perspective Toward Distributed Data Mining. Advances in Distributed and Parallel Knowledge Discovery, M IT/A A AI Press.
Bailey, S., Grossman, R., Sivakumar, H. and Turinsky, A. (1999). Papyrus: a system for data mining over local and wide area clusters and super-clusters. In Proc. Conference on Supercomputing, page 63. ACM Press.
Albashiri, K.A., Coenen, F.P, Sanderson, R. and Leng. P. (2007). Frequent Set Meta Mining: Towards Multi-Agent Data Mining. In Bramer, M., Coenen, F.P. and Petri dis, M. (Eds.), Research and Development in Intelligent Systems XXIII,, Springer, London, (proc, AI’2007), pp139–151.
Bellifemine, F. Poggi, A. and Rimassi, G. (1999). JADE: A FIPA-Compliant agent frame-work. Proc. Practical Applications of Intelligent Agents and Multi-Agents, pg 97–108 (See http://sharon.cselt.it/projects/jade for latest information).
Quinlan, J. R. and Cameron-Jones, R. M. (1993). FOIL: A Midterm Report. Proc. ECML, Vienna, Austria, pp3–20.
Coenen, F., Leng, P. and Zhang, L. (2005). Threshold Tuning for Improved Classification Association Rule Mining. Proceeding PAKDD 2005, LNAI3158, Springer, pp216–225.
Schollmeier, R. (2001). A Definition of Peer-to-Peer Networking for the Classification of Peer-to-Peer Architectures and Applications. First International Conference on Peer-to-Peer Computing (P2P01) IEEE.
Prodromides, A., Chan, P. and Stolfo, S. (2000). Meta-Learning in Distributed Data Mining Systems: Issues and Approaches. In Kargupta, H. and Chan, P. (Eds), Advances in Distributed and Parallel Knowledge Discovery. AAAI Press/The MIT Press, pp81–l 14.
Yin, X. and Han, J. (2003). CPAR: Classification based on Predictive Association Rules. Proc. SIAM Int. Conf. on Data Mining (SDM’03), San Fransisco, CA, pp. 331–335.
Li W., Han, J. and Pei, J. (2001). CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules. Proc ICDM 2001, pp369–376.
Liu, B. Hsu, W. and Ma, Y (1998). Integrating Classification and Assocoiation Rule Mining. Proceedings KDD-98, New York, 27–31 August. AAAI. pp80–86.
Blake, C.L. and Merz, C.J. (1998). UCI Repositoiy of machine learning databases http; //www.ics.uci.edu/mlearn/MLRepository.html, Irvine, CA: University of California, Department of Information and Computer Science.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag London Limited
About this paper
Cite this paper
Albashiri, K.A., Coenen, F., Leng, P. (2009). EMADS: An Extendible Multi-Agent Data Miner. In: Bramer, M., Petridis, M., Coenen, F. (eds) Research and Development in Intelligent Systems XXV. SGAI 2008. Springer, London. https://doi.org/10.1007/978-1-84882-171-2_19
Download citation
DOI: https://doi.org/10.1007/978-1-84882-171-2_19
Publisher Name: Springer, London
Print ISBN: 978-1-84882-170-5
Online ISBN: 978-1-84882-171-2
eBook Packages: Computer ScienceComputer Science (R0)