Overview
- Editors:
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Longbing Cao
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Faculty of Engineering and Information Technology, University of Technology, Sydney, Broadway, Australia
- Addresses the merger between two scientific areas: data mining and multi-agents
- Includes methodologies, techniques, algorithms, and systems
- Real-life applications and systems of multi-agents
- Provides new domain problems and knowledge for further research and development
- Written by leading researchers in this area
- Includes supplementary material: sn.pub/extras
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Table of contents (23 chapters)
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Front Matter
Pages i-xiii
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Introduction to Agents and Data Mining Interaction
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- Chayapol Moemeng, Vladimir Gorodetsky, Ziye Zuo, Yong Yang, Chengqi Zhang
Pages 47-58
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Data Mining Driven Agents
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- Shafiq Alam, Gillian Dobbie, Patricia Riddle
Pages 61-75
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- Philippe Fournier-Viger, Roger Nkambou, Usef Faghihi, Engelbert Mephu Nguifo
Pages 77-92
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- Doina Alexandra Dumitrescu, Ruth Cobos, Jaime Moreno-Llorena
Pages 93-102
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- Li-Tung Weng, Yue Xu, Yuefeng Li, Richi Nayak
Pages 103-126
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- Mei-Ling Shyu, Varsha Sainani
Pages 127-142
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- David F. Barrero, David Camacho, María D. R-Moreno
Pages 143-154
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- Jaime Moreno–Llorena, Xavier Alamán, Ruth Cobos
Pages 155-166
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- Markus Strohmaier, Mark Kröll, Peter Prettenhofer
Pages 167-176
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Agent Driven Data Mining
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Front Matter
Pages 188-188
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- Kamal Ali Albashiri, Frans Coenen
Pages 189-200
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- Igor Kiselev, Reda Alhajj
Pages 201-218
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- T. Ravindra Babu, M. Narasimha Murty, S. V. Subrahmanya
Pages 219-238
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- Daniela S. Santos, Denise de Oliveira, Ana L. C. Bazzan
Pages 239-249
About this book
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.
Reviews
From the reviews:
“This book promotes the latest methodological, technical, and practical advancements in the use of agents in data mining applications. … chapters include extensive bibliographies. … The book is intended for students, researchers, engineers, and practitioners, in both agent and data mining areas, who are interested in the potential of integrating agents and mining. … interested readers who are willing to make an effort to build on the book’s material will benefit from reading it.” (J. P. E. Hodgson, ACM Computing Reviews, December, 2009)
Editors and Affiliations
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Faculty of Engineering and Information Technology, University of Technology, Sydney, Broadway, Australia
Longbing Cao
