Introduction to Agent Mining
Purchasing Options
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.
