Data-Driven Discovery of Quantitative Rules in Relational Databases.
Jiawei Han, Yandong Cai, Nick Cercone:
Data-Driven Discovery of Quantitative Rules in Relational Databases.
IEEE Trans. Knowl. Data Eng. 5(1): 29-40(1993)@article{DBLP:journals/tkde/HanCC93,
author = {Jiawei Han and
Yandong Cai and
Nick Cercone},
title = {Data-Driven Discovery of Quantitative Rules in Relational Databases},
journal = {IEEE Trans. Knowl. Data Eng.},
volume = {5},
number = {1},
year = {1993},
pages = {29-40},
ee = {db/journals/tkde/HanCC93.html},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
Abstract
A quantitative rule is a rule associated with
quantitative information which assesses the
representativeness of the rule in the database.
In this paper, we develop an efficient induction method
for learning quantitative rules in relational databases.
With the assistance of knowledge about concept hierarchies,
data relevance, and expected rule forms, attribute-oriented
induction can be performed on the database, which
integrates database operations with the learning process
and provides a simple, efficient way of learning
quantitative rules from large databases. Our method
learns both characteristic rules and classification rules.
Quantitative information facilitates quantitative
reasoning, incremental learning, and learning in the
presence of noise. Moreover, learning qualitative rules
can be treated as a special case of learning quantitative
rules. Our paper shows that attribute-oriented induction
provides an efficient and effective mechanism for learning
various kinds of knowledge rules from relational databases.
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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Contents
Copyright © Tue Dec 1 16:37:41 2009
by Michael Ley (ley@uni-trier.de)
