Synopsis
Advances in Knowledge Discovery and Data Mining brings together the latest research—in statistics, databases, machine learning, and artificial intelligence —that are part of the exciting and rapidly growing field of Knowledge Discovery and Data Mining.
Advances in Knowledge Discovery and Data Mining brings together the latest research—in statistics, databases, machine learning, and artificial intelligence—that are part of the exciting and rapidly growing field of Knowledge Discovery and Data Mining. Topics covered include fundamental issues, classification and clustering, trend and deviation analysis, dependency modeling, integrated discovery systems, next generation database systems, and application case studies. The contributors include leading researchers and practitioners from academia, government laboratories, and private industry. The last decade has seen an explosive growth in the generation and collection of data. Advances in data collection, widespread use of bar codes for most commercial products, and the computerization of many business and government transactions have flooded us with data and generated an urgent need for new techniques and tools that can intelligently and automatically assist in transforming this data into useful knowledge. This book is a timely and comprehensive overview of the new generation of techniques and tools for knowledge discovery in data. Distributed for AAAI Press
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About the Author
Usama M. Fayyad heads the Machine Learning Systems Group at the Jet Propulsion Laboratory, California Institute of Technology and is an Adjunct Assistant Professor of Computer Science at the University of Southern California. Gregory Piatetsky-Shapiro is a Principal Member of Technical Staff and a Principal Investigator of KDD project at GTE Laboratories. Padhraic Smyth is a Technical Group Leader at the Jet Propulsion Laboratory. Ramasamy Uthurusamy is Machine Learning Project Leader at the General Motors R&D Center.
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