Data Mining and Multi-agent Integration
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This edited book reports on the state-of-the-art research and development progress in promoting the synergism of two cutting-edge technologies - agents and data mining. The volume presents the methodologies, algorithms and systems that integrate these two technologies. The text highlights:
- basic concepts and techniques in agents-data mining interaction and integration
- techniques to build data mining systems from agent perspectives, and the advantages of this approach
- how to enhance agents capabilities through data mining
- efficient and effective ways to integrate agents and data mining techniques
- case studies demonstrating the mutual enhancement of the integration
- theoretical and applied problems arising at the cross boundary of multiagent, data mining and KDD technologies
- new problems, challenges, and their impact on the future trends in these areas and in information technology as a whole.
- basic concepts and techniques in agents-data mining interaction and integration
- techniques to build data mining systems from agent perspectives, and the advantages of this approach
- how to enhance agents capabilities through data mining
- efficient and effective ways to integrate agents and data mining techniques
- case studies demonstrating the mutual enhancement of the integration
- theoretical and applied problems arising at the cross boundary of multiagent, data mining and KDD technologies
- new problems, challenges, and their impact on the future trends in these areas and in information technology as a whole.
Data Mining and Multi agent Integration aims to re?ect state of the art research and development of agent mining interaction and integration (for short, agent min ing). The book was motivated by increasing interest and work in the agents data min ing, and vice versa. The interaction and integration comes about from the intrinsic challenges faced by agent technology and data mining respectively; for instance, multi agent systems face the problem of enhancing agent learning capability, and avoiding the uncertainty of self organization and intelligence emergence. Data min ing, if integrated into agent systems, can greatly enhance the learning skills of agents, and assist agents with predication of future states, thus initiating follow up action or intervention. The data mining community is now struggling with mining distributed, interactive and heterogeneous data sources. Agents can be used to man age such data sources for data access, monitoring, integration, and pattern merging from the infrastructure, gateway, message passing and pattern delivery perspectives. These two examples illustrate the potential of agent mining in handling challenges in respective communities. There is an excellent opportunity to create innovative, dual agent mining interac tion and integration technology, tools and systems which will deliver results in one new technology.
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