- Publisher: MIT Press
- Format: Paperback | 625 pages
- Dimensions: 152mm x 226mm x 33mm | 862g
- Publication date: 5 March 1996
- Publication City/Country: Cambridge, Mass.
- ISBN 10: 0262560976
- ISBN 13: 9780262560979
- Edition statement: New.
- Illustrations note: index
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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Gregory Piatetsky-Shapiro is Senior Member of Technical Staff and Principal Investigator of the Knowledge Discovery Project at GTE Laboratories.
Table of contents
From data mining to knowledge discovery: an overview, Usama M. Fayyad etal. Part 1 Foundations: The process of knowledge discovery in databases: a human-centred approach, Ronald J. Brachman and Tej Anand; Graphical models for discovering knowledge, Wray Buntine; A statistical perspective on knowledge discovery in databases, John Elder IV and Daryl Pregibon. Part 2 Classification and clustering: Inductive logic programming and knowledge discovery in databases, Saso Dzeroski; Bayesian classification (autoclass): theory and results, Peter Cheeseman and John Stutz; Discovering informative patterns and data cleaning, Isabelle Guyon et al; Transforming rules and trees into comprehensive structures, Brian R. Gaines. Part 3 Trend and deviation analysis: Finding patterns in time series: a dynamic programming approach, Donald J. Berndt and James Clifford; explore: a multipattern and multistrategy discovery assistant, Willi Klosgen. Part 4 Dependency derivation: Bayesian networks for knowledge discovery, David Heckerman; Fast discovery of association rules, Rakesh Agrawal et al; From contingency tables to variation forms of knowledge in databases, Robert Zembowicz and Jan M. Zytkow. Part 5 Integrated discovery systems: Integrating inductive and deductive reasoning for data mining, Evangelos Simoudis, et al; Metaqueries for data mining, Wei-Min Shen et al; exploration of the power of attribute-oriented induction in data mining, Jiawei Han and Yongjian Fu. Part 6 Next generation database systems: Using inductive learning to generate rules for semantic query optimization, Chun-Nan Hsu and Craig A. Knoblock; Data surveyor: searching the nuggets in parallel, Marcel Holsheimer, et al. Part 7 Automating the analysis and cataloguing of sky surveys, Usama M. Fayyad et al; Selecting and reporting what is interesting: the KEFIR application to healthcare data, Christopher J. Matheus; Modeling subjective uncertainty in image annotation, Padhraic Smyth et al; Predicting equity returns from securities data with minimal rule generation, Chidanand Apte and Se June Hong; From data mining to knowledge discovery: current challenges and future directions, Ramasamy Uthurusamy. Appendices: Knowledge discovery in databases terminology; data mining, and knowledge discovery Internet resources, Gregory Piatetsky-Shapiro.