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A framework for Multi-Agent Based Clustering

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Abstract

A framework to support Multi-Agent Based Clustering (MABC) is described. A unique feature of the framework is that it provides mechanisms to allow agents to negotiate so as to improve an initial cluster configuration. The framework encourages a two phase approach to clustering. During the first phase clustering agents bid for records in the input data and form an initial cluster configuration. In the second phase (the negotiation phase) agents pass individual records to each other so as to improve the initial configuration. The communication framework and its operation is fully described in terms of the performatives used and from an algorithmic perspective. The reported evaluation was conducted using benchmark data sets. The results demonstrate that the supported agent negotiation produces enhanced clustering results.

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References

  1. Agogino, A., & Tumer, K. (2006). Efficient agent-based cluster ensembles. In Proceedings of 5th international conference on autonomous agents and multiagent systems, AAMAS ’06 (pp. 1079–1086). New York, NY: ACM.

  2. Albashiri K. A., Coenen F., Leng P. (2009) EMADS: An extendible multi-agent data miner. Knowledge-Based Systems 22(7): 523–528

    Article  Google Scholar 

  3. Babu, T., Murty, M., & Subrahmanya, S. (2010). Multiagent based large data clustering scheme for data mining applications. In Proceedings of 6th international conference on Active Media Technology (ACT’10) (pp. 116–127). Heidelberg: Springer LNAI.

  4. Bailey, S., Grossman, R., Sivakumar, H., & Turinsky, A. (1999). Papyrus: A system for data mining over local and wide area clusters and super-clusters. In Supercomputing, ACM/IEEE 1999 conference, Portland, OR.

  5. Bellifemine F., Bergenti F., Caire G., Poggi A. (2005) JADE: A java agent development framework. In: Bordini R. H., Dastani M., Dix J., Fallah-Seghrouchni A. (eds) Multi-agent programming: Languages, platforms, and applications. Springer, New York

    Google Scholar 

  6. Berkhin P. (2006) A survey of clustering data mining techniques. In: Kogan J., Nicholas C., Teboulle M. (eds) Grouping multidimensional data. Springer, Berlin, pp 25–71

    Chapter  Google Scholar 

  7. Cao L. (2009) Introduction to agent mining interaction and integration. In: Cao L. (eds) Data mining and multi-agent integration. Springer, Boston, pp 3–36

    Chapter  Google Scholar 

  8. Cao, L., Luo, C., & Zhang, C. (2007). Agent-mining interaction: An emerging area. In V. Gorodetsky, C. Zhang, V. Skormin, & L. Cao (Eds.), Autonomous intelligent systems: Multi-agents and data mining. Lecture Notes in Computer Science (Vol. 4476, pp. 60–73). Berlin: Springer.

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

    Article  Google Scholar 

  10. Chaimontree, S., Atkinson, K., & Coenen, F. (2010). Best clustering configuration metrics: Towards multiagent based clustering. In Proceedings of 6th international conference on advanced data mining and applications (ADMA’10). LNAI (Vol. 6440, pp. 48–59). Heidelberg: Springer.

  11. Chaimontree, S., Atkinson, K., & Coenen, F. (2010). Multi-agent based clustering: Towards generic multi-agent data mining. In Proceedings of 10th international conference on data mining. LNAI (Vol. 6171, pp. 115–127). Heidelberg: Springer.

  12. Committee of Public Accounts: Getting it right (2003). Improving decision-making and appeals in social security benefits. Tech. Report, Committee of Public Accounts. London: TSO, 2104 (House of Commons papers, session 2003/04; HC406).

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

    Google Scholar 

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

    Article  Google Scholar 

  15. Dhillon, I., & Modha, D. (2000). A data-clustering algorithm on distributed memory multiprocessors. In Proceedings of workshop on large-scale parallel KDD system, SIGKDD. LNCS (Vol. 1759, pp. 245–260). Berlin: Springer.

  16. Estivill-Castro, V. (2002). Why so many clustering algorithms: A position paper. SIGKDD Explorations Newsletter 4(1), 65–75. http://doi.acm.org/10.1145/568574.568575.

  17. FIPA. (2001). Communicative Act Library Specification. Tech. Rep. XC00037H, Foundation for Intelligent Physical Agents. http://www.fipa.org. Accessed 23 January 2011.

  18. Frank, A., & Asuncion, A. (2010). UCI machine learning repository. http://archive.ics.uci.edu/ml. Accessed 10 January 2011.

  19. Giannella, C., Bhargava, R., & Kargupta, H. (2004). Multi-agent systems and distributed data mining. In M. Klusch, S. Ossowski, V. Kashyap, & R. Unland (Eds.), Cooperative information agents VIII. Lecture Notes in Computer Science (Vol. 3191, pp. 1–15). Berlin: Springer.

  20. Groothius M., Svensson J. (2000) Expert system support and juridical quality. In: Breuker J., Leenes R., Winkels R. (eds) Legal knowledge and information systems. Jurix 2000: 13th annual conference. IOS Press, Amsterdam, pp 1–10

    Google Scholar 

  21. Halkidi M., Batistakis Y., Vazirgiannis M. (2001) On clustering validation techniques. Journal of Intelligent Information Systems 17: 107–145

    Article  MATH  Google Scholar 

  22. Januzaj, E., Kriegel, H., & Pfeifle, M. (2004). Scalable density based distributed clustering. In Proceedings of extending database technology (EDBT’04). LNCS (Vol. 3202, pp. 231–244). Berlin: Springer.

  23. Kargupta, H., Hamzaoglu, I., & Stafford, B. (1997). 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). Menlo Park, CA: AAAI Press.

  24. Kaufman L., Rousseeuw P. (1990) Finding groups in data: An introduction to cluster analysis. Wiley Interscience, New York

    Book  Google Scholar 

  25. Kiselev, I., & Alhajj, R. (2008). A self-organizing multi-agent system for online unsupervised learning in complex dynamic environments. In Proceedings of 23rd AAAI conference on artificial intelligence (pp. 1808–1809). Chicago, IL: AAAI Press.

  26. Klusch, M., Lodi, S., & Moro, G. (2003). The role of agents in distributed data mining: Issues and benefits. In Proceedings of IEEE/WIC international conference on intelligent agent technology (IAT ’03) (pp. 211–218),Seattle, USA.

  27. MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of 5th Berkeley symposium on mathematical statistics and probability (pp. 281–297). Berkeley, CA: University of California Press.

  28. McBurney P., Parsons S., Wooldridge M. (2002) Desiderata for agent argumentation protocols. In: Castelfranchi C., Johnson W.L. (eds) Proceedings of 1st international joint conference on autonomous agents and multi-agent systems (AAMAS’02). ACM Press, New York, pp 402–409

    Chapter  Google Scholar 

  29. Mitkas, P., Kehagias, D., Symeonidis, A., & Athanasiadis, I. (2004). A framework for constructing multi-agent applications and training intelligent agents. In P. Giorgini, J. Mller, & J. Odell (Eds.), Agent-oriented software engineering IV. Lecture Notes in Computer Science (Vol. 2935, pp. 255–290). Berlin: Springer.

  30. Moemeng C., Gorodetsky V., Zuo Z., Yang Y., Zhang C. (2009) Agent-based distributed data mining: A survey. In: Cao L. (eds) Data mining and multi-agent integration. Springer, Boston, pp 47–58

    Chapter  Google Scholar 

  31. National Audit Office. (2006). International benchmark of fraud and error in social security systems. Tech. Rep., the Controller and Auditor General, HC 1387 Session 2005–2006.

  32. National Bureau of Economic Research. (2009). Errors in the social security disability award process. http://www.nber.org/aginghealth/winter04/w10219.html. Accessed 14 August 2011.

  33. Olman V., Mao F., Wu H., Xu Y. (2009) Parallel clustering algorithm for large data sets with applications in bioinformatics. IEEE/ACM Transactions on Computational Biology and Bioinformatics 6(2): 311–352

    Article  Google Scholar 

  34. Pedrucz W. (2005) Knowledge-based clustering: From data to information granuals. Wiley-Interscience, Chichester

    Book  Google Scholar 

  35. Rao M. (1971) Clustering analysis and mathematical programming. Journal of the American statistical association 66(345): 622–626

    MATH  Google Scholar 

  36. Rao V.S. (2009) Multi agent-based distributed data mining: An overview. International Journal of Reviews in Computing, 3: 83–92

    Google Scholar 

  37. Reed, J. W., Potok, T. E., Patton, R. M. (2004). A multi-agent system for distributed cluster analysis. In Proceedings of third international workshop on software engineering for large-scale multi-agent systems (SELMAS’04) W16L workshop—26th international conference on software engineering (pp. 152–155). Edinburgh: IEE.

  38. Wooldridge M. (2009) An introduction to multiagent systems. 2nd edn. Wiley, Chichester

    Google Scholar 

  39. Xu R., Wunsch D. (2009) Clustering. Wiley/IEEE Press, Hoboken, NJ

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, The University of Liverpool, Liverpool, L69 3BX, UK

    Santhana Chaimontree, Katie Atkinson & Frans Coenen

Authors
  1. Santhana Chaimontree
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  2. Katie Atkinson
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  3. Frans Coenen
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Correspondence to Santhana Chaimontree.

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Chaimontree, S., Atkinson, K. & Coenen, F. A framework for Multi-Agent Based Clustering. Auton Agent Multi-Agent Syst 25, 425–446 (2012). https://doi.org/10.1007/s10458-011-9187-0

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