Knowledge Acquisition from Databases
Author : Xindong Wu
Publisher :
Page : 0 pages
File Size : 10,17 MB
Release : 1995
Category :
ISBN : 9785675020591
Author : Xindong Wu
Publisher :
Page : 0 pages
File Size : 10,17 MB
Release : 1995
Category :
ISBN : 9785675020591
Author : Qing Yan
Publisher :
Page : 122 pages
File Size : 37,53 MB
Release : 1993
Category : Concepts
ISBN :
Author : Xindong Wu
Publisher : Intellect Books
Page : 240 pages
File Size : 31,85 MB
Release : 1995
Category : Computers
ISBN :
A comprehensive English-Russian and Russian-English collection of modern statistical terminology, containing some 13,500 terms and some 1,000 names. Topics covered include mathematical statistics and probability theory, computational statistics and statistical software, and applied statistical components in economics, sociology, demography, medicine, natural sciences, and technology. The volume provides an extensive collection of terms in the fields of computer terminology related to problems of data processing and statistical software, theory of random processes, statistical quality control, operations research, and some supplementary areas such as the terminology of Russian official statistics. For translators and other experts who work with English and Russian statistical literature. Annotation copyright by Book News, Inc., Portland, OR
Author : George E. Lasker
Publisher : Windsor, Ont. : International Institute for Advanced Studies in Systems Research and Cybernetics
Page : 120 pages
File Size : 26,71 MB
Release : 1995
Category : Databases
ISBN : 9780921836339
Author : Ahmad A. Barfoursh
Publisher :
Page : pages
File Size : 26,13 MB
Release : 1989*
Category :
ISBN :
Author : Ujjwal Maulik
Publisher : Springer Science & Business Media
Page : 375 pages
File Size : 29,77 MB
Release : 2006-05-06
Category : Computers
ISBN : 1846282845
The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit,therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the following chapters.
Author : Joozar Khozem Vasi
Publisher :
Page : 244 pages
File Size : 26,38 MB
Release : 1992
Category : Knowledge acquisition (Expert systems)
ISBN :
Author : Joozar Khozem Vasi
Publisher : National Library of Canada = Bibliothèque nationale du Canada
Page : 244 pages
File Size : 24,79 MB
Release : 1992
Category : Knowledge acquisition (Expert systems)
ISBN :
Author : Gregory Piatetsky-Shapiro
Publisher : MIT Press
Page : 550 pages
File Size : 15,32 MB
Release : 1991
Category : Computers
ISBN :
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.
Author : Laurent Perez
Publisher :
Page : 140 pages
File Size : 38,37 MB
Release : 2001
Category : Data analysis
ISBN :