Systems for Knowledge Discovery in Databases.
Christopher J. Matheus, Philip K. Chan, Gregory Piatetsky-Shapiro:
Systems for Knowledge Discovery in Databases.
IEEE Trans. Knowl. Data Eng. 5(6): 903-913(1993)@article{DBLP:journals/tkde/MatheusCP93,
author = {Christopher J. Matheus and
Philip K. Chan and
Gregory Piatetsky-Shapiro},
title = {Systems for Knowledge Discovery in Databases},
journal = {IEEE Trans. Knowl. Data Eng.},
volume = {5},
number = {6},
year = {1993},
pages = {903-913},
ee = {db/journals/tkde/MatheusCP93.html},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX
Abstract
Knowledge-discovery systems face challenging problems
from real-world databases, which tend to be dynamic,
incomplete, redundant, noisy, sparse, and very large. These
problems are addressed and some techniques for handling them
are described. A model of an idealized knowledge-discovery
system is presented as a reference for studying and
designing new systems. This model is used in the comparison
of three systems: CoverStory, EXPLORA, and the Knowledge
Discovery Workbench. The deficiencies of existing systems
relative to the model reveal several open problems for
future research.
Copyright © 1993 by The Institute of
Electrical and Electronic Engineers, Inc. (IEEE).
Abstract used with permission.
CDROM Version: Load the CDROM "Volume 3 Issue 3, TKDE 1993-1995" and ...
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BibTeX
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BibTeX
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