Publications

HERSHEY, PA - July 27, 2009 - The growing complexity and volume of modern databases make it increasingly important for researchers and practitioners involved with association rule mining to make sense of the information they contain.

IGI Global’s newest release, Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection (edited by: Dr. Yun Sing Koh, Auckland University of Technology, New Zealand and Dr. Nathan Rountree, University of Otago, NZ) provides readers with an in-depth compendium of current issues, trends, and technologies in association rule mining. Covering a comprehensive range of topics, this book discusses underlying frameworks, mining techniques, interest metrics, and real-world application domains within the field.

"This book discusses the many issues surrounding association rules, including security, privacy, and incomplete and inaccurate data. It also details association rules and their application in various domains, including mobile mining, social networking, graph mining, etc..." writes Koh. "The Rare Association Rule Mining and Knowledge Discovery volume is essential reading for any researcher or Graduate student interested in this newly emerging and important area within the Association Rule Mining discipline. Data Mining practitioners will find the book a valuable addition to their tool-set due to its wide applicability in real-life environments."

Selected Chapters:

• Replacing Support in Association Rule Mining
• Effective Mining of Weighted Fuzzy Association Rules
• A Multi-Methodological Approach to Rare Association Rule Mining
• Discovering Interesting Patterns in Numerical Data with Background Knowledge
• Mining Unexpected Sequential Patterns and Implication Rules

Perpetual Access: US $270.00
Print + Perpetual: US $360.00

To learn more about this title, please see: http://www.igi-global.com/reference/details.asp?ID=34650.

For additional information about this publication, to arrange an interview with the editor, or to request a copy for review, please contact Gregory Guenther at gguenther@igi-global.com.

ABOUT THE EDITORS

Yun Sing Koh is currently a lecturer in computer science at Auckland University of Technology (New Zealand). After completing a bachelor's degree in computer science and master’s degree in software engineering at the University of Malaya, she went on to do her PhD in computer science in Otago, New Zealand. Her current research interests include data mining, machine learning, and information retrieval.

Nathan Rountree is a lecturer in computer science at the University of Otago (Dunedin, New Zealand), where he teaches papers on databases, data structures and algorithms, and Web development. He holds a bachelor's degree in music, a postgraduate diploma in computer science, and a PhD in computer science, all from Otago. His research interests include computer science education, artificial neural networks, and collaborative filtering.