Skip to main content

A Multi-agent Based Approach to Clustering: Harnessing the Power of Agents

  • Conference paper
Agents and Data Mining Interaction (ADMI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7103))

Included in the following conference series:

Abstract

A framework for multi-agent based clustering is described whereby individual agents represent individual clusters. A particular feature of the framework is that, after an initial cluster configuration has been generated, the agents are able to negotiate with a view to improving on this initial clustering. The framework can be used in the context of a number of clustering paradigms, two are investigated: K-means and KNN. The reported evaluation demonstrates that negotiation can serve to improve on an initial cluster configuration.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agogino, A., Tumer, K.: Efficient agent-based cluster ensembles. In: Proceedings of the 5th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2006, pp. 1079–1086. ACM, New York (2006)

    Google Scholar 

  2. Bailey, S., Grossman, R., Sivakumar, H., Turinsky, A.: Papyrus: A system for data mining over local and wide area clusters and super-clusters. IEEE Supercomputing (1999)

    Google Scholar 

  3. 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)

    Google Scholar 

  4. Cao, L., Gorodetsky, V., Mitkas, P.A.: Agent mining: The synergy of agents and data mining. IEEE Intelligent Systems 24(3), 64–72 (2009)

    Article  Google Scholar 

  5. Cao, L., Gorodetsky, V., Mitkas, P.A.: Guest editors’ introduction: Agents and data mining. IEEE Intelligent Systems 24(3), 14–15 (2009)

    Article  Google Scholar 

  6. Chaimontree, S., Atkinson, K., Coenen, F.: Best Clustering Configuration Metrics: Towards Multiagent Based Clustering. In: Cao, L., Feng, Y., Zhong, J. (eds.) ADMA 2010, Part I. LNCS, vol. 6440, pp. 48–59. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  7. Chaimontree, S., Atkinson, K., Coenen, F.: Clustering in a Multi-Agent Data Mining Environment. In: Cao, L., Bazzan, A.L.C., Gorodetsky, V., Mitkas, P.A., Weiss, G., Yu, P.S. (eds.) ADMI 2010. LNCS, vol. 5980, pp. 103–114. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  8. Chaimontree, S., Atkinson, K., Coenen, F.: Multi-Agent Based Clustering: Towards Generic Multi-Agent Data Mining. In: Perner, P. (ed.) ICDM 2010. LNCS, vol. 6171, pp. 115–127. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  9. Coenen, F., Leng, P., Ahmed, S.: T-trees, vertical partitioning and distributed association rule mining. In: Proceedings of the 3rd IEEE International Conference on Data Mining, ICDM 2003, pp. 513–516. IEEE Computer Society, Washington, DC, USA (2003)

    Google Scholar 

  10. Dasarathy, B.V.: Nearest neighbor (NN) norms: NN pattern classification techniques. IEEE Computer Society Press, Las Alamitos (1991)

    Google Scholar 

  11. Dasilva, J., Giannella, C., Bhargava, R., Kargupta, H., Klusch, M.: Distributed data mining and agents. Engineering Applications of Artificial Intelligence 18(7), 791–807 (2005)

    Article  Google Scholar 

  12. FIPA: Communicative Act Library Specification. Tech. Rep. XC00037H, Foundation for Intelligent Physical Agents (2001), http://www.fipa.org

  13. Forman, G., Zhang, B.: Distributed data clustering can be efficient and exact. ACM SIGKDD Explorations Newsletter 2, 34–38 (2000)

    Article  Google Scholar 

  14. Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml

  15. Fukanaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, New York (1972)

    Google Scholar 

  16. Giannella, C., Bhargava, R., Kargupta, H.: Multi-agent Systems and Distributed Data Mining. In: Klusch, M., Ossowski, S., Kashyap, V., Unland, R. (eds.) CIA 2004. LNCS (LNAI), vol. 3191, pp. 1–15. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  17. Kargupta, H., Chan, P. (eds.): Advances in Distributed and Parallel Knowledge Discovery. MIT Press, Cambridge (2000)

    Google Scholar 

  18. Kargupta, H., Hamzaoglu, I., Stafford, B.: Scalable, distributed data mining using an agent based architecture. In: Proceedings the 3rd International Conference on the Knowledge Discovery and Data Mining, pp. 211–214. AAAI Press (1997)

    Google Scholar 

  19. Kiselev, I., Alhajj, R.: A self-organizing multi-agent system for online unsupervised learning in complex dynamic environments. In: Proceedings of the 23rd AAAI Conference on Artificial Intelligence, pp. 1808–1809. AAAI Press (2008)

    Google Scholar 

  20. Klusch, M., Lodi, S., Moro, G.: Agent-Based Distributed Data Mining: The KDEC Scheme. In: Klusch, M., Bergamaschi, S., Edwards, P., Petta, P. (eds.) Intelligent Information Agents. LNCS (LNAI), vol. 2586, pp. 104–122. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  21. Klusch, M., Lodi, S., Moro, G.: The role of agents in distributed data mining: Issues and benefits. In: IAT 2003: Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology, p. 211. IEEE Computer Society, Washington, DC, USA (2003)

    Chapter  Google Scholar 

  22. MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)

    Google Scholar 

  23. McBurney, P., Parsons, S., Wooldridge, M.: Desiderata for agent argumentation protocols. In: Castelfranchi, C., Johnson, W.L. (eds.) Proceedings of the 1st Int. Conf. on Autonomous Agents and Multi-Agent Systems (AAMAS 2002), pp. 402–409. ACM Press, New York (2002)

    Google Scholar 

  24. Moemeng, C., Gorodetsky, V., Zuo, Z., Yang, Y., Zhang, C.: Agent-based distributed data mining: A survey. In: Cao, L. (ed.) Data Mining and Multi-agent Integration, pp. 47–58. Springer, US (2009)

    Chapter  Google Scholar 

  25. Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 1–15 (2004)

    Article  Google Scholar 

  26. Park, B.H., Kargupta, H.: Distributed data mining: Algorithms, Systems, and Applications. In: Data Mining Handbook, pp. 341–358. IEA (2002)

    Google Scholar 

  27. Provost, F.: Distributed data mining: Scaling up and beyond. In: Advances in Distributed and Parallel Knowledge Discovery, pp. 3–27. MIT Press (1999)

    Google Scholar 

  28. Rao, M.: Clustering analysis and mathematical programming. Journal of the American Statistical Association 66(345), 622–626 (1971)

    Article  MATH  Google Scholar 

  29. Reed, J.W., Potok, T.E., Patton, R.M.: A multi-agent system for distributed cluster analysis. In: Proceedings of the 3rd International Workshop on Software Engineering for Large-Scale Multi-Agent Systems (SELMAS 2004) W16L Workshop - 26th International Conference on Software Engineering, pp. 152–155. IEE, Edinburgh (2004)

    Google Scholar 

  30. Xu, R., Wunsch, D.: Clustering. Wiley/IEEE Press (2009)

    Google Scholar 

  31. Younis, O., Fahmy, S.: Distributed clustering in ad-hoc sensor networks: a hybrid, energy-efficient approach. In: 23rd Annual Joint Conf. of the IEEE Computer and Communications Societies, INFOCOM 2004, vol. 1, pp. 629–640 (2004)

    Google Scholar 

  32. Zaki, M.J., Ho, C.-T. (eds.): KDD 1999. LNCS (LNAI), vol. 1759. Springer, Heidelberg (2000)

    Google Scholar 

  33. Zaki, M.J., Pan, Y.: Introduction: Recent developments in parallel and distributed data mining. Distributed Parallel Databases 11, 123–127 (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chaimontree, S., Atkinson, K., Coenen, F. (2012). A Multi-agent Based Approach to Clustering: Harnessing the Power of Agents. In: Cao, L., Bazzan, A.L.C., Symeonidis, A.L., Gorodetsky, V.I., Weiss, G., Yu, P.S. (eds) Agents and Data Mining Interaction. ADMI 2011. Lecture Notes in Computer Science(), vol 7103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27609-5_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-27609-5_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27608-8

  • Online ISBN: 978-3-642-27609-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics