By Peter Stone (auth.), Longbing Cao, Ana L. C. Bazzan, Andreas L. Symeonidis, Vladimir I. Gorodetsky, Gerhard Weiss, Philip S. Yu (eds.)
This publication 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 self sustaining brokers and Multiagent structures.
The eleven revised complete papers offered have been rigorously reviewed and chosen from 24 submissions. The papers are prepared in topical sections on brokers for info mining; facts mining for brokers; and agent mining applications.
Read or Download 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 PDF
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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
K. B. Patel operations are performed based on the type and availability of the distributed resources. Grigorios Tsoumakas and Ioannis Vlahavas  have shown different phases in a typical architecture of a DDM approach. In the first phase local distributed databases are analyzed. Then, the discovered knowledge is transmitted to a merger site, where all the distributed local models are integrated. The global knowledge is then transmitted back to update the distributed databases. In some cases, instead of having a merger site, the local models are broadcasted to all other sites, so that each site can compute the global model in parallel.
Al.  proposed mobile-agent-based distributed knowledge discovery architecture called, knowledge discovery management system (KDMS) for data mining in the distributed, heterogeneous database systems. The architecture contains some knowledge discovery sub-systems (sub-KDS). KDMS is located in the management system of the distributed database system. The sub-KDS is located in each site of the distributed database. Based on the architecture of the distributed knowledge discovery system a flexible and efficient mobile-agent-based distributed algorithm (IDMA) for association rules is presented, in which the global association rules and all the local association can be mined at the same time.
They can monitor traffic in large networks and learn about the trouble spots in the network. Based on the experiences of the agent in the network the agent can choose better routes to reach the next host. • MA system allows an application to scale well, since the number of participating hosts can be increased without any significant impact on the complexity of the application. The parent agent can also clone several child agents to implement concurrent operations, and raise running efficiency. • MAs are naturally heterogeneous and goal oriented in nature.
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.)