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About this product
- DescriptionThe 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 Kwledge Discovery: Techlogies for Infrequent and Critical Event Detection provides readers with an in-depth compendium of current issues, trends, and techlogies 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.
- Author BiographyYun 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.
- PublisherIGI Global
- Date of Publication15/08/2009
- SubjectComputing: Professional & Programming
- Place of PublicationHershey
- Country of PublicationUnited States
- ImprintInformation Science Reference
- Content Note1, black & white illustrations
- Weight1029 g
- Width216 mm
- Height280 mm
- Spine19 mm
- Edited byNathan Rountree,Yun Sing Koh
- Format DetailsLaminated cover
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