Abstract
In this paper we: introduce EMADS, the Extendible Multi-Agent Data mining System, to support the dynamic creation of communities of data mining agents; explore the capabilities of such agents and demonstrate (by experiment) their application to data mining on distributed data. Although, EMADS is not restricted to one data mining task, the study described here, for the sake of brevity, concentrates on agent based Association Rule Mining (ARM), in particular what we refer to as frequent set meta mining (or Meta ARM). A full description of our proposed Meta ARM model is presented where we describe the concept of Meta ARM and go on to describe and analyse a number of potential solutions in the context of EMADS. Experimental results are considered in terms of: the number of data sources, the number of records in the data sets and the number of attributes represented.
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Kamber, M., Winstone, L., Wan, G., Shan, S. and Jiawei, H., “Generalization and Decision Tree Induction: Efficient Classification in Data Mining”. Proc. of the Seventh International Workshop on Research Issues in Data Engineering, pp. 111-120, 1997.
Aggarwal, C. C. and Yu, P. S., “A Condensation Approach to Privacy Preserving Data Mining”. Lecture Notes in Computer Science, Vol. 2992, pp. 183-199, 2004
Wooldridge, M., “An Introduction to Multi-Agent Systems”. John Wiley and Sons Ltd, paperback, 366 pages, ISBN 0-471-49691-X, 2002.
Stolfo, S., Prodromidis, A. L., Tselepis, S. and Lee, W., “JAM: Java Agents for Meta-Learning over Distributed Databases”. Proc. of the International Conference on Knowledge Discovery and Data Mining, pp. 74-81, 1997.
Xu, L. and Jordan, M. I., “EM learning on a generalised finite mixture model for combining multiple classifiers”, In Proc. of World Congress on Neural Networks, 1993.
Guo, Y. and Sutiwaraphun, J., “Knowledge probing in distributed data mining”. In Advances in Distributed and Parallel Knowledge Discovery, 1999.
Breiman, L., Bagging predictors, Machine Learning, 24, 123-140, 1996.
Agrawal, R., Imielinski, T., and Swami A., “Mining Association Rules between Sets of Items in Large Databases”. In Proc. of ACM SIGMOD Conference on Management of Data, Washington DC, May 1993.
Goulbourne, G., Coenen, F.P. and Leng, P., “Algorithms for Computing Association Rules Using A Partial-Support Tree”. Proc. ES99, Springer, London, pp. 132-147, 1999.
Coenen, F.P. Leng, P., and Goulbourne, G., “Tree Structures for Mining Association Rules”. Journal of Data Mining and Knowledge Discovery, Vol 8, No 1, pp. 25-51, 2004.
Kargupta, H., Hamzaoglu, I., and Stafford, B., “Scalable, Distributed Data Mining Using an Agent Based Architecture”. Proc. of Knowledge Discovery and Data Mining, AAAI Press, 211-214,1997.
Kargupta, H., Hersh berger, D., and Johnson, E., Collective Data Mining: A New Perspective Toward Distributed Data Mining. Advances in Distributed and Parallel Knowledge Discovery, MIT/AAAI Press, 1999.
Bailey S., Grossman, R., Sivakumar, H., and Turinsky, A., “Papyrus: a system for data mining over local and wide area clusters and super-clusters”. In Proc. Conference on Supercomputing, page 63. ACM Press, 1999.
Albashiri, K.A., Coenen, F.P., Sanderson, R. and Leng, P., Frequent Set Meta Mining: Towards Multi-Agent Data Mining. To appear in Research and Development in Intelligent Systems XXIV, Springer, London, (proc. AI’2007).
Schollmeier, R., “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. August 2001.
Bellifemine, F., Poggi, A., and Rimassi, G., “JADE: A FIPA-Compliant agent framework”. Proc. Practical Applications of Intelligent Agents and Multi-Agents, April 1999, pg 97-108 (See http://sharon.cselt.it/projects/jade for latest information) “ Mining System”. Proc. 2nd Int. Conf. Knowledge Discovery and Data Mining, (KDD1996).
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Albashiri, K.A., Coenen, F., Leng, P. (2008). Agent Based Frequent Set Meta Mining: Introducing EMADS. In: Bramer, M. (eds) Artificial Intelligence in Theory and Practice II. IFIP AI 2008. IFIP – The International Federation for Information Processing, vol 276. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09695-7_3
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DOI: https://doi.org/10.1007/978-0-387-09695-7_3
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