Abstract
A framework for Multi Agent Data Mining (MADM) is described. The framework comprises a collection of agents cooperating to address given data mining tasks. The fundamental concept underpinning the framework is that it should support generic data mining. The vision is that of a system that grows in an organic manner. The central issue to facilitating this growth is the communication medium required to support agent interaction. This issue is partly addressed by the nature of the proposed architecture and partly through an extendable ontology; both are described. The advantages offered by the framework are illustrated in this paper by considering a clustering application. The motivation for the latter is that no “best” clustering algorithm has been identified, and consequently an agent-based approach can be adopted to identify “best” clusters. The application serves to demonstrates the full potential of MADM.
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
Kargupta, H., Hamzaoglu, I., Stafford, B.: Scalable, distributed data mining using an agent based architecture. In: Proceedings the Third International Conference on the Knowledge Discovery and Data Mining, pp. 211–214. AAAI Press, Menlo Park (1997)
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 of Supercomputing. IEEE, Los Alamitos (1999)
Czarnowski, I., Jȩdrzejowicz, P.: Agent-based non-distributed and distributed clustering. In: Perner, P. (ed.) Machine Learning and Data Mining in Pattern Recognition. LNCS (LNAI), vol. 5632, pp. 347–360. Springer, Heidelberg (2009)
Baazaoui Zghal, H., Faiz, S., Ben Ghezala, H.: A framework for data mining based multi-agent: An application to spatial data. In: Proceedings - WEC 2005: 3rd World Enformatika Conference, vol. 5, pp. 22–26 (2005)
Albashiri, K., Coenen, F., Leng, P.: Emads: An extendible multi-agent data miner. Knowledge-Based Systems 22(7), 523–528 (2009)
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)
Dasarathy, B.V.: Nearest neighbor (NN) norms: NN pattern classification techniques. IEEE Computer Society Press, Los Alamitos (1991)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: 2nd International conference on Knowledge Discovery and Data Mining (KDD 1996), pp. 226–231 (1996)
van Rijsbergen, C.: Information Retrieval, 2nd edn. Butterworths, London (1979)
Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley, Reading (2005)
Bellifemine, F., Bergenti, F., Caire, G., Poggi, A.: Jade: a java agent development framework. In: Bordini, R.H. (ed.) Multi-agent programming: languages, platforms, and applications, p. 295. Springer, New York (2005)
Austin, J.L. (ed.): How to do Things with Words. Oxford University Press, Oxford (1962)
Searle, J.: Speech Acts: An Essay in the Philosophy of Language. Cambridge University Press, Cambridge (1969)
Patil, R., Fikes, R.F., Patel-Schneider, P.F., McKay, D., Finin, T., Gruber, T., Neches, R.: The DARPA knowledge sharing effort: Progress report. In: Nebel, B., Rich, C., Swartout, W. (eds.) Principles of Knowledge Representation and Reasoning: Proceedings of the Third International Conference, pp. 777–788. Morgan Kaufmann, USA (1992)
FIPA: Communicative Act Library Specification. Technical Report XC00037H, Foundation for Intelligent Physical Agents (2001), http://www.fipa.org
Asuncion, A., Newman, D.: UCI machine learning repository (2007)
McBurney, P., Parsons, S., Wooldridge, M.: Desiderata for agent argumentation protocols. In: Castelfranchi, C., Johnson, W.L. (eds.) Proceedings of the First International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2002), Bologna, Italy, pp. 402–409. ACM Press, New York (2002)
Bench-Capon, T., Dunne, P.E.: Argumentation in artificial intelligence. Artificial Intelligence 171(10-15), 619–641 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chaimontree, S., Atkinson, K., Coenen, F. (2010). Multi-Agent Based Clustering: Towards Generic Multi-Agent Data Mining. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2010. Lecture Notes in Computer Science(), vol 6171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14400-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-14400-4_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14399-1
Online ISBN: 978-3-642-14400-4
eBook Packages: Computer ScienceComputer Science (R0)