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.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

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

Reviews & Statements

Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection discusses the many issues surrounding association rules, including security, privacy, and incomplete and inaccurate data. This book also details association rules and their application in various domains, including mobile mining, social networking, graph mining, etc.

– Yun Sing Koh, AUT, New Zealand

Table of Contents

Search this Book:

Reset

Editor/Author Biographies

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.

Abstracting & Indexing

Archiving

All IGI Global Scientific Publishing content is archived via the CLOCKSS and LOCKSS initiative. Additionally, all IGI Global Scientific Publishing published content is available in the IGI Global Scientific Publishing InfoSci^® platform.

Editorial Advisory Board

Editorial Advisory Board

Richard A O’Keefe, University of Otago, New Zealand

Russel Pears, AUT University, New Zealand

Nathan Rountree (Editor), Auckland University of Technology, New Zealand

David Taniar, Monash University, Australia

List of Reviewers

Markus Breitenbach, Northpointe Institute for Public Management, USA

Giulia Bruno, Politecnico di Torino, Italy

Adrian Fan, University of Colorado, USA

Szymon Jaroszewicz, National Insitute of Telecommunication, Warsaw, Poland

Yun Sing Koh, AUT University, New Zealand

Dong (Haoyuan) Li, University of Montpellier, France

Rangsipan Marukatat, Mahidol University, Thailand

Maybin Muyeba, Manchester Metropolitan University, UK

Rosa Meo, Dipartimento di Informatica Università degli Studi di Torino, Italy

Agathe Merceron, Media Informatics department, University of Applied Sciences - TFH Berlin, Germany

Richard O'Keefe, Department of Computer Science, University of Otago, New Zealand

Dave Parry, AUT University, New Zealand

Russel Pears, AUT University, New Zealand

Nathan Rountree, Department of Computer Science, University of Otago, New Zealand

Huaifeng Zhang, University of Technology, Sydney (UTS), Australia

Mengjie Zhang, University of Victoria, New Zealand