Author: Piatetski-Shapiro, Gregory & William Frawley (Eds)
Title: Knowledge Discovery in Databases.
Description: Cambridge, MA: MIT Press, 1991. Paperback. 539 pp.- 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. English text. Condition : as new. Mailorder only - Alleen verzending mogelijk. Book condition : as new. ISBN 9780262660709.

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