Knowledge Discovery in Databases

ISBN 10: 0262660709 / ISBN 13: 9780262660709

Published by MIT Press, 1992

About this Item

Former library copy. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Seller Inventory # GRP21270192

Bibliographic Details

• Title: Knowledge Discovery in Databases
• Publisher: MIT Press
• Publication Date: 1992
• Language: English
• ISBN 10: 0262660709
• ISBN 13: 9780262660709
• Binding: Soft cover
• Condition: Good
• Weight: 2.857 pounds

About this title

Synopsis

Knowledge Discovery in Databases brings together current research on the exciting problem of discovering useful and interesting knowledge in databases. It spans many different approaches to discovery, including inductive learning, bayesian statistics, semantic query optimization, knowledge acquisition for expert systems, information theory, and fuzzy 1 sets.The rapid growth in the number and size of databases creates a need for tools and techniques for intelligent data understanding. Relationships and patterns in data may enable a manufacturer to discover the cause of a persistent disk failure or the reason for consumer complaints. But today's databases hide their secrets beneath a cover of overwhelming detail. The task of uncovering these secrets is called discovery in databases. This loosely defined subfield of machine learning is concerned with discovery from large amounts of possible uncertain data. Its techniques range from statistics to the use of domain knowledge to control search.Following an overview of knowledge discovery in databases, thirty technical chapters are grouped in seven parts which cover discovery of quantitative laws, discovery of qualitative laws, using knowledge in discovery, data summarization, domain specific discovery methods, integrated and multi-paradigm systems, and methodology and application issues. An important thread running through the collection is reliance on domain knowledge, starting with general methods and progressing to specialized methods where domain knowledge is built in.

Gregory Piatetski-Shapiro is Senior Member of Technical Staff and Principal Investigator of the Knowledge Discovery Project at GTE Laboratories. William Frawley is Principal Member of Technical Staff at GTE and Principal Investigator of the Learning in Expert Domains Project.

About the Author

Gregory Piatetsky-Shapiro is Senior Member of Technical Staff and Principal Investigator of the Knowledge Discovery Project at GTE Laboratories.