Rare Association Rule Mining and Knowledge Discovery
Technologies for Infrequent and Critical Event Detection
Published on 31. August 2009
Book
Hardback
301 pages
978-1-60566-754-6 (ISBN)
Description
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.
More details
Language
English
Place of publication
Hershey
United States
Publishing group
IGI Global
Target group
College/higher education
Professional and scholarly
Illustrations
Illustrations
Dimensions
Height: 286 mm
Width: 221 mm
Thickness: 22 mm
Weight
1068 gr
ISBN-13
978-1-60566-754-6 (9781605667546)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
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.
Content
Creating risk-scores Effective mining of association rules Filtering association rules Imbalanced data sets Mining rare association rules Mining unexpected sequential patterns Multi-methodological approach in rule mining Quasi-functional dependencies Rare association rule mining Strong symmetric association rules Weighted fuzzy association rules