Skip to main content
SpringerLink
Log in
Book cover

International Workshop on Agents and Data Mining Interaction

ADMI 2009: Agents and Data Mining Interaction pp 53–68Cite as

Agent-Enriched Data Mining Using an Extendable Framework

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5680))

Abstract

This paper commences with a discussion of the advantages that Multi-Agent Systems (MAS) can bring to the domain of Knowledge Discovery in Data (KDD), and presents a rational for Agent-Enriched Data Mining (AEDM). A particular challenge of any generic, general purpose, AEDM system is the extensive scope of KDD. To address this challenge the authors suggest that any truly generic AEDM must be readily extendable and propose EMADS, The Extendable Multi-Agent Data mining System. A complete overview of the architecture and agent interaction models of EMADS is presented. The system’s operation is described and illustrated in terms of two KDD scenarios: meta association rule mining and classifier generation. In conclusion the authors suggest that EMADS provides a sound foundation for both KDD research and application based AEDM.

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

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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aggarwal, C., Yu, P.: A Condensation Approach to Privacy Preserving DataMining. In: Bertino, E., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K., Ferrari, E. (eds.) EDBT 2004. LNCS, vol. 2992, pp. 183–199. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  2. Agrawal, R., Mehta, M., Shafer, J., Srikant, R., Arning, A., Bollinger, T.: The Quest Data Mining System. In: Proceedings 2nd Int. Conf. Knowledge Discovery and Data Mining, KDD (1996)

    Google Scholar 

  3. Albashiri, K., Coenen, F., Sanderson, R., Leng, P.: Frequent Set Meta Mining: Towards Multi-Agent Data Mining. In: Bramer, M., Coenen, F.P., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIII, pp. 139–151. Springer, London (2007)

    Google Scholar 

  4. Albashiri, K., Coenen, F., Leng, P.: Agent Based Frequent Set Meta Mining: Introducing EMADS. In: Artificial Intelligence in Theory and Practice II, Proceedings IFIP, pp. 23–32. Springer, Heidelberg (2007)

    Google Scholar 

  5. Baazaoui, H., Faiz, S., Ben Hamed, R., Ben Ghezala, H.: A Framework for data mining based multi-agent: an application to spatial data. In: 3rd World Enformatika Conference, WEC 2005, Avril, Istanbul (2005)

    Google Scholar 

  6. 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 Conference on Supercomputing, p. 63. ACM Press, New York (1999)

    Google Scholar 

  7. Bellifemine, F., Poggi, A., Rimassi, G.: JADE: A FIPA-Compliant agent framework. In: Proceedings Practical Applications of Intelligent Agents and Multi-Agents, pp. 97–108 (1999), http://sharon.cselt.it/projects/jade

  8. Blake, C., Merz, C.: UCI Repository of machine learning databases. University of California, Department of Information and Computer Science, Irvine, CA (1998), http://www.ics.uci.edu/mlearn/MLRepository.html

  9. Bose, R., Sugumaran, V.: IDM: An Intelligent Software Agent Based DataMining Environment. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, pp. 2888–2893. IEEE Press, San Diego (1998)

    Google Scholar 

  10. Bota, J., Gmez-Skarmeta, A., Valds, M., Padilla, A.: Metala: A meta-learning architecture. Fuzzy Days, 688–698 (2001)

    Google Scholar 

  11. Cao, L., Zhang, C.: F-Trade: Agent-mining symbiont for financial services. In: AAMAS, pp. 1363–1364 (2007)

    Google Scholar 

  12. Coenen, F., Leng, P., Goulbourne, G.: Tree Structures for Mining Association Rules. Journal of Data Mining and KDD 8, 25–51 (2004)

    Article  MathSciNet  Google Scholar 

  13. Coenen, F., Leng, P., Zhang, L.: Threshold Tuning for Improved Classification Association Rule Mining. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS, vol. 3518, pp. 216–225. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  14. Foundation for Intelligent Physical Agents, FIPA 2002 Specification. Geneva, Switzerland (2002), http://www.fipa.org/specifications/index.html

  15. Giuseppe, D., Giancarlo, F.: A customizable multi-agent system for distributed data mining. In: Proc. of the 2007 ACM symp. on applied computing, pp. 42–47 (2007)

    Google Scholar 

  16. Gorodetsky, V., Karsaeyv, O., Samoilov, V.: Multi-agent technology for distributed data mining and classification. In: Proceedings Int. Conf. on Intelligent Agent Technology (IAT 2003), IEEE/WIC, pp. 438–441 (2003)

    Google Scholar 

  17. Grossman, R., Turinsky, A.: A framework for finding distributed data mining strategies that are intermediate between centralized strategies and in-place strategies. In: KDD Workshop on Distributed Data Mining (2000)

    Google Scholar 

  18. Kargupta, H., Byung-Hoon, et al.: Collective Data Mining: A New Perspective Toward Distributed Data Mining. In: Advances in Distributed and Parallel Knowledge Discovery. MIT/AAAI Press (1999)

    Google Scholar 

  19. Kargupta, H., Hamzaoglu, I., Stafford, B.: Scalable, Distributed Data Mining Using an Agent Based Architecture. In: Proceedings of Knowledge Discovery and Data Mining, pp. 211–214. AAAI Press, Menlo Park (1997)

    Google Scholar 

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

    Chapter  Google Scholar 

  21. Liu, B., Hsu, W., Ma, Y.: Integrating Classification and Assocoiation Rule Mining. In: Proceedings KDD 1998, New York, August 27-31, pp. 80–86. AAAI, Menlo Park (1998)

    Google Scholar 

  22. Li, W., Han, J., Pei, J.: CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules. In: Proceedings ICDM, pp. 369–376 (2001)

    Google Scholar 

  23. Luo, P., Huang, R., He, Q., Lin, F., Shi, Z.: Execution engine of meta-learning system for kdd in multi-agent environment. Technical report, Institute of Computing Technology. Chinese Academy of Sciences (2005)

    Google Scholar 

  24. METAL Project. Esprit Project METAL (2002), http://www.metal-kdd.org

  25. Peng, S., Mukhopadhyay, S., Raje, R., Palakal, M., Mostafa, J.: A Comparison Between Single-agent and Multi-agent Classification of Documents. In: Proceedings 15th Intern. PD Processing Symposium, pp. 935–944 (2001)

    Google Scholar 

  26. Prodromides, A., Chan, P., Stolfo, S.: Meta-Learning in Distributed Data Mining Systems: Issues and Approaches, pp. 81–114. AAAI Press/The MIT Press (2000)

    Google Scholar 

  27. Quinlan, J.R., Cameron-Jones, R.M.: FOIL: A Midterm Report. In: Brazdil, P.B. (ed.) ECML 1993. LNCS, vol. 667, pp. 3–20. Springer, Heidelberg (1993)

    Google Scholar 

  28. Shi, Z., Zhang, H., Cheng, Y., Jiang, Y., Sheng, Q., Zhao, Z.: Mage: An agent-oriented programming environment. In: Proceedings of the IEEE International Conference on Cognitive Informatics, pp. 250–257 (2004)

    Google Scholar 

  29. Stolfo, S., Prodromidis, A.L., Tselepis, S., Lee, W.: JAM: Java Agents for Meta-Learning over Distributed Databases. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining, pp. 74–81 (1997)

    Google Scholar 

  30. Vilalta, R., Christophe, G., Giraud-Carrier, B.P., Soares, C.: Using Meta-Learning to Support Data Mining. IJCSA 1(1), 31–45 (2004)

    Google Scholar 

  31. Yin, X., Han, J.: CPAR: Classification based on Predictive Association Rules. In: Proc. SIAM Int. Conf. on Data Mining (SDM 2003), SF, CA, pp. 331–335 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, The University of Liverpool, Ashton Building, Ashton Street, Liverpool, L69 3BX, United Kingdom

    Kamal Ali Albashiri & Frans Coenen

Authors
  1. Kamal Ali Albashiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frans Coenen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of IT, University of Technology, Broadway, P.O. Box 123, 2007, Sydney, NSW, Australia

    Longbing Cao

  2. St. Petersburg Intitute for Informaticsand Automation, 39, 14-th Liniya, 199178, St. Petersburg, Russia

    Vladimir Gorodetsky

  3. Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong, Hong Kong

    Jiming Liu

  4. Software Competence Center Hagenberg GmbH, Softwarepark 21, 4232, Hagenberg, Austria

    Gerhard Weiss

  5. Department of Computer Science, University of Illinois at Chicago, 851 S. Morgan St., Rm 1138 SEO, 60607, Chicago, IL, USA

    Philip S. Yu

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Albashiri, K.A., Coenen, F. (2009). Agent-Enriched Data Mining Using an Extendable Framework. In: Cao, L., Gorodetsky, V., Liu, J., Weiss, G., Yu, P.S. (eds) Agents and Data Mining Interaction. ADMI 2009. Lecture Notes in Computer Science(), vol 5680. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03603-3_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-03603-3_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03602-6

  • Online ISBN: 978-3-642-03603-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation