CALL FOR CHAPTER PROPOSALS
Proposal Submission Deadline Extended: June 20, 2008
Rare Association Rule Mining and Knowledge Discovery:
Technologies for Infrequent and Critical Event Detection
(Advances in Data Warehousing and Mining Book Series)
A book edited by Yun Sing Koh , Auckland University of Technology , New
Zealand
Nathan Rountree, University of Otago, New Zealand
Introduction
The growing complexity and volume of modern databases make it increasingly
important for us to make sense of the information they contain. Most research
in the area of association rule mining has focused on the sub-problem of
efficient frequent rule generation. However in some data mining applications
relatively infrequent associations are likely to be of great interest as they
relate to rare but crucial cases. The main focus of rare association rules is to
allow the detection of infrequent events that have dramatic consequences.
Examples of mining rare rules include identifying relatively rare diseases,
predicting telecommunication equipment failure, and finding associations
between infrequently purchased supermarket items. Indeed, rare rules warrant
special attention because they are more difficult to find using traditional data
mining techniques.
Objective of the Book
Rare Association Rule Mining and Knowledge Discovery: Technologies for
Infrequent and Critical Event Detection will aim to provide readers in-depth
knowledge on the current issues in rare association rule mining. The book is
designed to cover a comprehensive range of topics related to rare association
rule mining: the underlying framework, mining techniques, interest metrics, and
real-world application domains of rare association mining.
Target Audience
The target audience of this book includes members of the general public,
computing professionals, and researchers interested in data mining.
Researchers involved in association rule mining will find the treatments of
infrequent and critical event detection particularly valuable. Since this book
will cover the issues of rare association rule mining comprehensively, it will be
usable as supplementary material at a graduate level.
Recommended topics include, but are not limited to, the following:
Pre-processing and Noise Detection
Beyond the Support-Confidence Framework
Data Partition-Based Rare Rule Mining
Mining Rare Rules with Categorical Attributes
Mining Rare Rules with Quantitative Attributes
Constraint-Based Rare Association Rule Mining
Other Rare Association Rule Mining Methods
Post-pruning in Rare Association Rule Mining
Integrating AI and Rare Association Rule Mining
Interest Metrics
Other issues
Applications
Submission Procedure
Researchers and practitioners are invited to submit on or before June 20,
2008, a 2-3 page chapter proposal clearly explaining the mission and concerns
of his or her proposed chapter. Authors of accepted proposals will be notified
by June 30, 2008 about the status of their proposals and sent chapter
guidelines. Full chapters are expected to be submitted by August 31, 2008. All
submitted chapters will be reviewed on a double-blind review basis. The book
is scheduled to be published by IGI Global (formerly Idea Group Inc.), www.igi-
global.com, publisher of the IGI Publishing (formerly Idea Group Publishing),
Information Science Publishing, IRM Press, CyberTech Publishing, Information
Science Reference (formerly Idea Group Reference), and Medical Information
Science Reference imprints. This book is part of the Advances in Data
Warehousing and Mining Book Series, found at www.igi-global.com/ADWM.
Inquiries and submissions can be forwarded electronically (Word document) or
by mail to:
Dr. Yun Sing Koh
School of Computing and Mathematical Sciences
Auckland University of Technology,
Private Bag 92006, Auckland 1142, New Zealand
Tel.: + +649 921 9999 Extn: 5068 • Fax: +649 921 9944
E-mail: [EMAIL PROTECTED]
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