Data Mining and Multi-agent Integration

  • Longbing Cao

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Introduction to Agents and Data Mining Interaction

    1. Front Matter
      Pages 1-1
    2. Chayapol Moemeng, Vladimir Gorodetsky, Ziye Zuo, Yong Yang, Chengqi Zhang
      Pages 47-58
  3. Data Mining Driven Agents

    1. Front Matter
      Pages 60-60
    2. Shafiq Alam, Gillian Dobbie, Patricia Riddle
      Pages 61-75
    3. Philippe Fournier-Viger, Roger Nkambou, Usef Faghihi, Engelbert Mephu Nguifo
      Pages 77-92
    4. Li-Tung Weng, Yue Xu, Yuefeng Li, Richi Nayak
      Pages 103-126
    5. David F. Barrero, David Camacho, María D. R-Moreno
      Pages 143-154
    6. Jaime Moreno–Llorena, Xavier Alamán, Ruth Cobos
      Pages 155-166
  4. Agent Driven Data Mining

    1. Front Matter
      Pages 188-188
    2. Kamal Ali Albashiri, Frans Coenen
      Pages 189-200
    3. T. Ravindra Babu, M. Narasimha Murty, S. V. Subrahmanya
      Pages 219-238
    4. Daniela S. Santos, Denise de Oliveira, Ana L. C. Bazzan
      Pages 239-249
    5. Stanislaw A. B. Stane, Mariusz Zytniewsk
      Pages 291-304
    6. Ana Carolina Bertoletti De Marchi, Márcia Cristina Moraes
      Pages 305-314
    7. Springer Science+Business Media, LLC
      Pages 329-329
  5. Back Matter
    Pages 155-161

About this book


Data Mining and Multi-agent Integration presents cutting-edge research, applications and solutions in data mining, and the practical use of innovative information technologies written by leading international researchers in the field. Topics examined include:

  • Integration of multiagent applications and data mining
  • Mining temporal patterns to improve agents behavior
  • Information enrichment through recommendation sharing
  • Automatic web data extraction based on genetic algorithms and regular expressions
  • A multiagent learning paradigm for medical data mining diagnostic workbench
  • A multiagent data mining framework
  • Streaming data in complex uncertain environments
  • Large data clustering
  • A multiagent, multi-objective clustering algorithm
  • Interactive web environment for psychometric diagnostics
  • Anomalies detection on distributed firewalls using data mining techniques
  • Automated reasoning for distributed and multiple source of data
  • Video contents identification

Data Mining and Multi-agent Integration is intended for students, researchers, engineers and practitioners in the field, interested in the synergy between agents and data mining. This book is also relevant for readers in related areas such as machine learning, artificial intelligence, intelligent systems, knowledge engineering, human-computer interaction, intelligent information processing, decision support systems, knowledge management, organizational computing, social computing, complex systems, and soft computing.


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

Editors and affiliations

  • Longbing Cao
    • 1
  1. 1.Faculty of Engineering and Information TechnologyUniversity of Technology, SydneyBroadwayAustralia

Bibliographic information