Knowledge Discovery and Data Mining: Challenges and Realities

Xingquan Zhu (University of Vermont, USA) and Ian Davidson (State University of New York at Albany, USA)

Publication Status: E-Book and Print Version Available for Purchase

ISBN13: 9781599042527

EISBN13: 9781599042541

DOI: 10.4018/978-1-59904-252-7

Description:

Knowledge discovery and data mining (KDD) is dedicated to exploring meaningful information from a large volume of data. Knowledge Discovery and Data Mining: Challenges and Realities is the most comprehensive reference publication for researchers and real-world data mining practitioners to advance knowledge discovery from low-quality data. This Premier Reference Source presents in-depth experiences and methodologies, providing theoretical and empirical guidance to users who have suffered from underlying, low-quality data. International experts in the field of data mining have contributed all-inclusive chapters focusing on interdisciplinary collaborations among data quality, data processing, data mining, data privacy, and data sharing.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

Challenges of data mining
Correlation mining
Dempster-Shafer theory
Image mining and diagnosis systems
Mining clinical trial data
Multifactor dimensionality reduction
Ontology Engineering
Outlier Detection
Predictive Analytics
Rough Set Theory
Software quality modeling

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Reviews

"This book advances the existing data mining methodologies towards practical and real-world usage through case studies and empirical analysis."

– Xingquan Zhu, University of Vermont, USA

International experts in teh field of data mining and knowledge discovery record their experiences, methodologies, and theories in this extensive reference guide for those who plod through low-quality data. This book includes topics such as data processing, data quality, data mining, data sharing, and data privacy.

– Kathy Dempsey, Computers in Libraries, November/December 2007, Vol. 27 No. 10

Xingquan Zhu is an assistant professor in the Department of Computer Science and Engineering at Florida Atlantic University, Boca Raton, FL. He received his Ph.D. in computer science from Fudan University, Shanghai, China, in 2001. From February 2001 to October 2002, he was a postdoctoral associate in the Department of Computer Science, Purdue University, West Lafayette, IN. From October 2002 to July 2006, he was a research assistant professor in the Department of Computer Science, University of Vermont, Burlington, VT. His research interests include data mining, machine learning, data quality, multimedia systems and information retrieval. Since 2000, Dr. Zhu has published extensively, including over 50 refereed papers in various journals and conference proceedings.

Ian Davidson is currently an assistant professor of computer science at the State University of New York (SUNY) at Albany. Prior to this appointment he worked in Silicon Valley most recently for SGI’s MineSet datamining group. He publishes and serves on the program committees of most AI and data mining conferences. He has a Ph.D. from Monash University under the supervision of C.S. Wallace.

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