Mining

Agents and Data Mining Interaction: 7th International by Peter Stone (auth.), Longbing Cao, Ana L. C. Bazzan, Andreas

By Peter Stone (auth.), Longbing Cao, Ana L. C. Bazzan, Andreas L. Symeonidis, Vladimir I. Gorodetsky, Gerhard Weiss, Philip S. Yu (eds.)

This booklet constitutes the completely refereed post-workshop lawsuits of the seventh overseas Workshop on brokers and knowledge Mining interplay, ADMI 2011, held in Taipei, Taiwan, in may well 2011 together with AAMAS 2011, the tenth foreign Joint convention on independent brokers and Multiagent platforms.
The eleven revised complete papers awarded have been rigorously reviewed and chosen from 24 submissions. The papers are equipped in topical sections on brokers for info mining; information mining for brokers; and agent mining applications.

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103–114. Springer, Heidelberg (2010) 8. : Multi-Agent Based Clustering: Towards Generic Multi-Agent Data Mining. In: Perner, P. ) ICDM 2010. LNCS, vol. 6171, pp. 115–127. Springer, Heidelberg (2010) 28 S. Chaimontree, K. Atkinson, and F. Coenen 9. : 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) 10. : Nearest neighbor (NN) norms: NN pattern classification techniques.

M. com 3 Department of Computer Sc. & Engineering, DCR University of Sc. com Abstract. Distributed Data Mining (DDM) is concerned with application of the classical Data Mining (DM) approach in a Distributed Computing (DC) environments so that the available resource including communication networks, computing units and distributed data repositories, human factors etc. can be utilized in a better way and on-line, real-time decision support based distributed applications can be designed. A Mobile Agent (MA) is an autonomous transportable program that can migrate under its own or host control from one node to another in a heterogeneous network.

FIPA: Communicative Act Library Specification. Tech. Rep. org 13. : Distributed data clustering can be efficient and exact. ACM SIGKDD Explorations Newsletter 2, 34–38 (2000) 14. edu/ml 15. : Introduction to Statistical Pattern Recognition. Academic Press, New York (1972) 16. : Multi-agent Systems and Distributed Data Mining. , Unland, R. ) CIA 2004. LNCS (LNAI), vol. 3191, pp. 1–15. Springer, Heidelberg (2004) 17. , Chan, P. ): Advances in Distributed and Parallel Knowledge Discovery. MIT Press, Cambridge (2000) 18.

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