Intelligent Information Technology (iiT) encompasses the theories and ap plications of artificial intelligence, statistical pattern recognition, learning theory, data warehousing, data mining and knowledge discovery, Grid com puting, and autonomous agents and multi-agent systems in the context of today's as well as future IT, such as Electronic Commerce (EC), Business Intelligence (BI), Social Intelligence (SI), Web Intelligence (WI), Knowledge Grid (KG), and Knowledge Community (KC), among others. The multi-author monograph presents the current state of the research and development in intelligent technologies for information analysis, in par ticular, advances in agents, data mining, and learning theory, from both the oretical and application aspects. It investigates the future of information technology (IT) from a new intelligent IT (iiT) perspective, and highlights major iiT-related topics by structuring an introductory chapter and 22 sur vey/research chapters into 5 parts: (1) emerging data mining technology, (2) data mining for Web intelligence, (3) emerging agent technology, ( 4) emerging soft computing technology, and (5) statistical learning theory. Each chapter includes the original work of the author(s) as well as a comprehensive survey related to the chapter's topic. This book will become a valuable source of reference for R&D profession als active in advanced intelligent information technologies. Students as well as IT professionals and ambitious practitioners concerned with advanced in telligent information technologies will appreciate the book as a useful text enhanced by numerous illustrations and examples.
Inhaltsverzeichnis
1. The Alchemy of Intelligent IT (iIT): A Blueprint for Future Information Technology. - I. Emerging Data Mining Technology. - 2. Grid-Based Data Mining and Knowledge Discovery. - 3. The MiningMart Approach to Knowledge Discovery in Databases. - 4. Ensemble Methods and Rule Generation. - 5. Evaluation Scheme for Exception Rule/Group Discovery. - 6. Data Mining for Targeted Marketing. - II. Data Mining for Web Intelligence. - 7. Mining for Information Discovery on the Web: Overview and Illustrative Research. - 8. Mining Web Logs for Actionable Knowledge. - 9. Discovery of Web Robot Sessions Based on Their Navigational Patterns. - 10. Web Ontology Learning and Engineering: An Integrated Approach. - 11. Browsing Semi-Structured Texts on the Web Using Formal Concept Analysis. - 12. Graph Discovery and Visualization from Textual Data. - III. Emerging Agent Technology. - 13. Agent Networks: Topological and Clustering Characterization. - 14. Finding the Best Agents for Cooperation. - 15. Constructing Hybrid Intelligent Systems for Data Mining from Agent Perspectives. - 16. Making Agents Acceptable to People. - IV. Emerging Soft Computing Technology. - 17. Constraint-Based Neural Network Learning for Time Series Predictions. - 18. Approximate Reasoning in Distributed Environments. - 19. Soft Computing Pattern Recognition, Data Mining and Web Intelligence. - 20. Dominance-Based Rough Set Approach to Knowledge Discovery (I): General Perspective. - 21. Dominance-Based Rough Set Approach to Knowledge Discovery (II): Extensions and Applications. - V. Statistical Learning Theory. - 22. Bayesian Ying Yang Learning (I): A Unified Perspective for Statistical Modeling. - 23. Bayesian Ying Yang Learning (II): A New Mechanism for Model Selection and Regularization. - Author Index.
