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

ISBN-10: 3642276083

ISBN-13: 9783642276088

ISBN-10: 3642276091

ISBN-13: 9783642276095

This booklet constitutes the completely refereed post-workshop complaints of the seventh overseas Workshop on brokers and information Mining interplay, ADMI 2011, held in Taipei, Taiwan, in may perhaps 2011 along side AAMAS 2011, the tenth foreign Joint convention on independent brokers and Multiagent structures.
The eleven revised complete papers provided have been conscientiously reviewed and chosen from 24 submissions. The papers are geared up in topical sections on brokers for facts mining; facts mining for brokers; and agent mining applications.

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Extra info for Agents and Data Mining Interaction: 7th International Workshop on Agents and Data Mining Interation, ADMI 2011, Taipei, Taiwan, May 2-6, 2011, Revised Selected Papers

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In a classical knowledge discovery technique in distributed environment, a single central repository called Data Warehouse (DW) is created and then DM tools are used to mine the data and extract the knowledge [3]. This approach, however, is ineffective or infeasible for a number of reasons [4] like (a) Storage, Computational and Communication cost involved to handle and store the data form the ever increasing and updated distributed L. Cao et al. ): ADMI 2011, LNAI 7103, pp. 30–45, 2012. © Springer-Verlag Berlin Heidelberg 2012 Agent Enriched Distributed Association Rules Mining: A Review 31 resources; (b) Undesirable central collection and utilization of privacy-sensitive data by the business organizations; (c) Performance and Scalability; (d) Resource constraints issues of distributed and mobile environment are not considered properly in central DW based DM [5].

207–216 (May 1993) 11. : Fast algorithms for mining association rules. In: Proc. 1994 Int. Conf. Very Large Data Bases, Santiago, Chile, pp. 487–499 (September 1994) 12. : Parallel Algorithm for Mining Frequent Itemsets. In: Proc. of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, August 18-21 (2005) 13. : Parallel and Distributed Association Mining: A Survey, Department of Computer Science. Rensselaer Polytechnic Institute, Troy 14. : Mobile Agents: An Introduction.

A Multi-agent Based Approach to Clustering 19 clustering result. To the above list of specific MADM agents we can also add a number of housekeeping agents that are utilised within the MADM framework. Data agents are the “owners” of data sources. There is a one-to-one relationship between data agents and data sources. Data agents can be thought of as the conduit whereby clustering agents can access data. Clustering agents are the “owners” of clusters. Groups of clustering agents can be though of as representing a clustering algorithm.

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Agents and Data Mining Interaction: 7th International Workshop on Agents and Data Mining Interation, ADMI 2011, Taipei, Taiwan, May 2-6, 2011, Revised Selected Papers by Peter Stone (auth.), Longbing Cao, Ana L. C. Bazzan, Andreas L. Symeonidis, Vladimir I. Gorodetsky, Gerhard Weiss, Philip S. Yu (eds.)


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