Introduction to Agent Mining

Introduction to Agent Mining

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ISBN 9781439800133
Cat# K10027
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Features

  • Presents the methodologies, techniques, algorithms, and systems that encompass multiagent systems and data mining
  • Includes agent-driven data mining algorithms for designing intelligent data mining methods
  • Offers case studies that highlight the effective integration of multiagent systems and data mining
  • Explores future trends in these fields

Summary

Multiagent systems and data mining have emerged as two of the most vigorous areas for building intelligent information technology. This book presents the synergy between these fields, along with the methodologies, techniques, algorithms, and systems that integrate these technologies. It highlights the effectiveness and power of agent-driven data mining algorithms in designing intelligent data mining methods, data mining-driven agent intelligence enhancement, and mutual challenges faced by both communities. Detailed case studies demonstrate the mutual enhancement of the integration while the impact of new problems and challenges shows future trends in information technology as a whole.

Table of Contents

Challenges and Prospects of Agents and Data Mining Interaction

Introduction to intelligent agents and multiagent systems

Introduction to data mining and knowledge discovery

Challenges in agents and data mining

Prospects of agent and data mining interaction

Mutual enhancement of agent and data mining through interaction

A New Area: Agents and Data Mining Interaction and Integration

Research map of agents and data mining interaction

Methodologies for agents and data mining interaction

State-of-the-art agents and data mining interaction

Trends of agents and data mining interaction

Agent-Enriched Knowledge Discovery and Data Mining

Advantages of agent-enriched knowledge discovery

Agent-based next-generation KDD infrastructure

Agent-enriched data mining process and management

Agent-enriched data mining methods

Agent-enriched distributed and multiple source data mining

Automated and agent-human-cooperated data mining learning

Case studies

Data Mining-Driven Agent Intelligence Enhancement

Advantages of data mining-driven multiagent systems

Data mining-driven agent coordination, adaptation, and evolution

Data mining-driven multi-agent communication, planning, and dispatching

Data mining-driven user modeling and servicing

Data mining-driven multiagent learning

Case studies

Common Issues in Agents and Data Mining

Prospects of tackling common issues

Involving user preferences and human intelligence

Involving domain knowledge and intelligence

Involving network intelligence

Involving organizational and social intelligence

Meta-synthesis of computing intelligence

Case studies

Agents-Mining Symbiont Performance Evaluation

Performance framework for agent-mining symbiont

Interestingness metrics for agent miners

Performance metrics for data-mining driven agent intelligence and behavior

Agents and Data Mining Integration Applications and Systems

State-of-the-art agents and data mining symbionts

Typical engineering and industry applications

Concluding Remarks

Author Bio(s)

Longbing Cao is a senior lecturer in the Faculty of Engineering and Information Technology at the University of Technology in Australia. Dr. Cao is the founder of the first knowledge portal and special interest group on Agents and Data Mining Interaction and Integration (AMII): www.agentmining.org

Chengqi Zhang is a research professor in the Faculty of Engineering and Information Technology at the University of Technology in Australia. A member of numerous professional organizations, Dr. Zhang has published more than 200 refereed papers in renowned international journals, such as Artificial Intelligence, Information Systems, IEEE Transactions, and ACM Transactions.

Zili Zhang is a senior lecturer in the School of Engineering and Information Technology at Deakin University in Australia and a professor in the Faculty of Computer and Information Science at Southwest University in China. Dr. Zhang has authored or co-authored several books and more than 80 refereed papers in international journals and conference proceedings. His research interests include agent-based computing, hybrid intelligent systems, and artificial intelligence.

 
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