Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction (Premier Reference Source)

Yanchang Zhao, Yanchang Zhao, Chengqi Zhang, Longbing Cao

ISBN 10: 1605664049 / ISBN 13: 9781605664040

Verlag: Information Science Reference, 2009

Beschreibung

372 pp., Hardcover, small publisher's handstamp to rear pastedown, else fine. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.

Bestandsnummer des Verkäufers ZB680884

Bibliografische Details

• Titel: Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction (Premier Reference Source)
• Verlag: Information Science Reference
• Erscheinungsjahr: 2009
• Sprache: Englisch
• ISBN-10: 1605664049
• ISBN-13: 9781605664040
• Einband: Hardcover
• Zustand: Fine

Über diesen Titel

Inhaltsangabe

There is often a large number of association rules discovered in data mining practice, making it difficult for users to identify those that are of particular interest to them. Therefore, it is important to remove insignificant rules and prune redundancy as well as summarize, visualize, and post-mine the discovered rules. Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction provides a systematic collection on post-mining, summarization and presentation of association rules, and new forms of association rules. This book presents researchers, practitioners, and academicians with tools to extract useful and actionable knowledge after discovering a large number of association rules.

Über die Autorin bzw. den Autor

Yanchang Zhao is a Postdoctoral Research Fellow in Data Sciences & Knowledge Discovery Research Lab, Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering & IT, University of Technology, Sydney, Australia. His research interests focus on association rules, sequential patterns, clustering and post-mining. He has published more than 30 papers on the above topics, including six journal articles and two book chapters. He served as a chair of two international workshops, and a program committee member for 11 international conferences and a reviewer for 8 international journals and over a dozen of international conferences. Chengqi Zhang is a Research Professor in Faculty of Engineering & IT, University of Technology, Sydney, Australia. He is the director of the Director of UTS Research Centre for Quantum Computation and Intelligent Systems and a Chief Investigator in Data Mining Program for Australian Capital Markets on Cooperative Research Centre. He has been a chief investigator of eight research projects. His research interests include Data Mining and Multi-Agent Systems. He is a co-author of three monographs, a co-editor of nine books, and an author or co-author of more than 150 research papers. He is the chair of the ACS (Australian Computer Society) National Committee for Artificial Intelligence and Expert Systems, a chair/member of the Steering Committee for three international conference. Longbing Cao is an Associate Professor in Faculty of Engineering & IT, University of Technology, Sydney (Australia). He is the Director of Data Sciences & Knowledge Discovery Research Lab. His research interest focuses on domain driven data mining, multi-agents, and the integration of agent and data mining. He is a chief investigator of two ARC (Australian Research Council) Discovery projects and one ARC Linkage project. He has over 50 publications, including one monograph, two edited books and 10 journal articles. He is a program co-chair of 11 international conferences.