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: [log in to unmask] ---------------------------------------------- CLASS-L list. Instructions: http://www.classification-society.org/csna/lists.html#class-l