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

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

Yun Sing Koh (Auckland University of Technology, New Zealand) and Nathan Rountree (University of Otago, NZ)
Indexed In: SCOPUS
Release Date: August, 2009|Copyright: © 2010 |Pages: 320
DOI: 10.4018/978-1-60566-754-6
ISBN13: 9781605667546|ISBN10: 1605667544|EISBN13: 9781605667553
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Description & Coverage
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.

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 and Testimonials

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
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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.
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Editorial Review 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