Back Cover

   

data mining: opportunities and challenges
Data Mining: Opportunities and Challenges
by John Wang (ed)  ISBN:1591400511
Idea Group Publishing © 2003 (468 pages)

In this text, an international team of 44 data mining experts specifically explore new methodologies or examine case studies in this new and multi-disciplinary topic.

Table of Contents Back Cover  

Back Cover

Data Mining: Opportunities and Challenges presents an overview of the state-of-the-art approaches in this new and multi-disciplinary field of data mining. This book explores the myriad issues regarding data mining, specifically focusing on those areas that explore new methodologies or examine case studies. This book contains numerous chapters written by an international team of forty-four experts representing leading scientists and talented young scholars from seven different countries.

About the Editor

John Wang is a professor in the Department of Information and Decision Sciences at Montclair State University (MSU). He completed his Ph.D. in Operations Research from Temple University in 1990, and worked as an assistant professor at Beijing University of Sciences & Technology, China, for two years. Dr. Wang has published 72 papers in refereed journals and conference proceedings, as well as two research books, and has been an active member of five renowned professional organizations. He was further invited to serve as a referee for Operations Research (a flagship journal) and IEEE Transactions on Control Systems Technology (a very prestigious journal). His current research interests include optimization, nonlinear programming, and manufacturing systems engineering.

 

   

data mining: opportunities and challenges
Data Mining: Opportunities and Challenges
by John Wang (ed)  ISBN:1591400511
Idea Group Publishing © 2003 (468 pages)

In this text, an international team of 44 data mining experts specifically explore new methodologies or examine case studies in this new and multi-disciplinary topic.

Table of Contents Back Cover  
Table of Contents
Data Mining—Opportunities and Challenges
Preface
Chapter I - A Survey of Bayesian Data Mining
Chapter II - Control of Inductive Bias in Supervised Learning Using Evolutionary Computation—A Wrapper-Based Approach
Chapter III - Cooperative Learning and Virtual Reality-Based Visualization for Data Mining
Chapter IV - Feature Selection in Data Mining
Chapter V - Parallel and Distributed Data Mining through Parallel Skeletons and Distributed Objects
Chapter VI - Data Mining Based on Rough Sets
Chapter VII - The Impact of Missing Data on Data Mining
Chapter VIII - Mining Text Documents for Thematic Hierarchies Using Self-Organizing Maps
Chapter IX - The Pitfalls of Knowledge Discovery in Databases and Data Mining
Chapter X - Maximum Performance Efficiency Approaches for Estimating Best Practice Costs
Chapter XI - Bayesian Data Mining and Knowledge Discovery
Chapter XII - Mining Free Text for Structure
Chapter XIII - Query-By-Structure Approach for the Web
Chapter XIV - Financial Benchmarking Using Self-Organizing Maps—Studying the International Pulp and Paper Industry
Chapter XV - Data Mining in Health Care Applications
Chapter XVI - Data Mining for Human Resource Information Systems
Chapter XVII - Data Mining in Information Technology and Banking Performance
Chapter XVIII - Social, Ethical and Legal Issues of Data Mining
Chapter XIX - Data Mining in Designing an Agent-Based DSS
Chapter XX - Critical and Future Trends in Data Mining—A Review of Key Data Mining Technologies/Applications
Index
List of Figures
List of Tables