Rare Association Rule Mining and Knowledge Discovery : Technologies for Infrequent and Critical Event Detection

Koh, Yun Sing; Rountree, Nathan

ISBN 10: 1605667544 / ISBN 13: 9781605667546

Published by Information Science Reference, 2009

About this Item

Unread book in perfect condition.

Seller Inventory # 6881282

Bibliographic Details

• Title: Rare Association Rule Mining and Knowledge Discovery : Technologies for Infrequent and Critical Event Detection
• Publisher: Information Science Reference
• Publication Date: 2009
• Language: English
• ISBN 10: 1605667544
• ISBN 13: 9781605667546
• Binding: Hardcover
• Condition: As New

About this title

Synopsis

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. Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection 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.

About the Author

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.