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

This ebook constitutes the completely refereed post-workshop court cases of the seventh overseas Workshop on brokers and knowledge Mining interplay, ADMI 2011, held in Taipei, Taiwan, in may perhaps 2011 at the side of AAMAS 2011, the tenth overseas Joint convention on self sustaining brokers and Multiagent structures.
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 information mining; information 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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BidDistance = W GADj (see Section 6) 7. IF ∃cj ∈ C such that bidDistance < cohesion threshold, allocate d to cj so as to minimise bidDistance 8. ELSE Allocate d to “outlier cluster” 9. IF no “successful” bids end loop 6 Cluster Configuration Metrics In order for agents to bid for examples some appropriate metric must be adopted. The process for deciding when and how to move a record also requires recourse to some metric, as does deciding when to split and merge clusters. Essentially there are three ways of measuring the “goodness” of a proposed cluster configuration.

B. , output of the serialization process [19], and 6) External state for intermediate results. The MA paradigm provides the following key advantages: • MAs reduce the network traffic and save the network bandwidth by allowing only shared resultant data to be carried over the network. • Some of the processor and memory intensive tasks of a program can be offloaded to other more powerful and not heavily loaded platforms due to agent’s mobility [21]. • Protocol in a distributed system can be encapsulated within a MA and any up gradation can be easily delivered and deployed to all locations using MA.

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. IEEE Computer Society Press, Las Alamitos (1991) 11. : Distributed data mining and agents. Engineering Applications of Artificial Intelligence 18(7), 791–807 (2005) 12. FIPA: Communicative Act Library Specification.

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