Data Mining and Multi-agent Integration

1. Front Matter

   Pages i-xiii

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2. Introduction to Agents and Data Mining Interaction

   1. Front Matter

      Pages 1-1

      PDF

   2. Introduction to Agent Mining Interaction and Integration

      • Longbing Cao

      Pages 3-36

   3. Towards the Integration of Multiagent Applications and Data Mining

      • Célia Ghedini Ralha

      Pages 37-46

   4. Agent-Based Distributed Data Mining: A Survey

      • Chayapol Moemeng, Vladimir Gorodetsky, Ziye Zuo, Yong Yang, Chengqi Zhang

      Pages 47-58

3. Data Mining Driven Agents

   1. Front Matter

      Pages 60-60

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   2. Exploiting Swarm Behaviour of Simple Agents for Clustering Web Users’ Session Data

      • Shafiq Alam, Gillian Dobbie, Patricia Riddle

      Pages 61-75

   3. Mining Temporal Patterns to Improve Agents Behavior: Two Case Studies

      • Philippe Fournier-Viger, Roger Nkambou, Usef Faghihi, Engelbert Mephu Nguifo

      Pages 77-92

   4. A Multi-Agent System for Extracting and Analysing Users’ Interaction in a Collaborative Knowledge Management System

      • Doina Alexandra Dumitrescu, Ruth Cobos, Jaime Moreno-Llorena

      Pages 93-102

   5. Towards Information Enrichment through Recommendation Sharing

      • Li-Tung Weng, Yue Xu, Yuefeng Li, Richi Nayak

      Pages 103-126

   6. A Multiagent-based Intrusion Detection System with the Support of Multi-Class Supervised Classification

      • Mei-Ling Shyu, Varsha Sainani

      Pages 127-142

   7. Automatic Web Data Extraction Based on Genetic Algorithms and Regular Expressions

      • David F. Barrero, David Camacho, María D. R-Moreno

      Pages 143-154

   8. Establishment and Maintenance of a Knowledge Network by Means of Agents and Implicit Data

      • Jaime Moreno–Llorena, Xavier Alamán, Ruth Cobos

      Pages 155-166

   9. Equipping Intelligent Agents with Commonsense Knowledge acquired from Search Query Logs: Results from an Exploratory Story

      • Markus Strohmaier, Mark Kröll, Peter Prettenhofer

      Pages 167-176

   10. A Multi-Agent Learning Paradigm for Medical Data Mining Diagnostic Workbench

       • Sam Chao, Fai Wong

       Pages 177-186

4. Agent Driven Data Mining

   1. Front Matter

      Pages 188-188

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   2. The EMADS Extendible Multi-Agent Data Mining Framework

      • Kamal Ali Albashiri, Frans Coenen

      Pages 189-200

   3. A Multiagent Approach to Adaptive Continuous Analysis of Streaming Data in Complex Uncertain Environments

      • Igor Kiselev, Reda Alhajj

      Pages 201-218

   4. Multiagent Systems for Large Data Clustering

      • T. Ravindra Babu, M. Narasimha Murty, S. V. Subrahmanya

      Pages 219-238

   5. A Multiagent, Multiobjective Clustering Algorithm

      • Daniela S. Santos, Denise de Oliveira, Ana L. C. Bazzan

      Pages 239-249

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.

• AAMAS
• Clustering
• agent-enriched data mining
• algorithm
• algorithms
• automated reasoning
• classification
• currentjm
• data mining
• filtering
• genetic algorithms
• knowledge management
• learning
• multi-agent systems
• regular expressions

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)

• Faculty of Engineering and Information Technology, University of Technology, Sydney, Broadway, Australia

  Longbing Cao

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