Discovery of Decision Rules in Relational Databases: A Rough Set Approach.
Xiaohua Hu, Nick Cercone:
Discovery of Decision Rules in Relational Databases: A Rough Set Approach.
CIKM 1994: 392-400@inproceedings{DBLP:conf/cikm/HuC94,
author = {Xiaohua Hu and
Nick Cercone},
title = {Discovery of Decision Rules in Relational Databases: A Rough
Set Approach},
booktitle = {Proceedings of the Third International Conference on Information
and Knowledge Management (CIKM'94), Gaithersburg, Maryland, November
29 - December 2, 1994},
publisher = {ACM},
year = {1994},
pages = {392-400},
ee = {db/conf/cikm/HuC94.html, http://doi.acm.org/10.1145/191246.191313},
crossref = {DBLP:conf/cikm/94},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX
Abstract
We develop an attribute-oriented rough set approach for the discovery of decision rules in relational
databases. Our approach combines machine learning techniques and rough set theory. We consider
a learning procedure to consist of the two phases data generalization and data reduction. In the data
generalization phase, utilizing knowledge about concept hierarchies and relevance of the data, an
attribute-oriented induction is performed attribute by attribute. Some undesirable attributes of the
discovery task are removed and the primitive data in the databases are generalized to the desirable
level; this process greatly decreases the number of tuples which must be examined for the discovery
task and substantially reduces the computational complexity of the database learning processes.
Subsequently, in data reduction phase, rough set theory is applied to the generalized relation; the
cause-effect relationships among the condition and decision attributes in the databases are analyzed
and the non-essential or irrelevant attributes to the discovery task are eliminated without losing
information of the original database system. This process further reduces the generalized relation.
Thus very concise and more accurate decision rules for each class in the decision attribute with little
or no redundancy information, can be extracted automatically from the reduced relation during the
learning process. Our study shows that attribute-oriented induction combined with rough set theory
provide an efficient and effective mechanism for discovering decision rules in database systems.
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BibTeX
Printed Edition
Proceedings of the Third International Conference on Information and Knowledge Management (CIKM'94), Gaithersburg, Maryland, November 29 - December 2, 1994.
ACM 1994
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Referenced by
- Xiaohua Hu, Nick Cercone:
Mining Knowledge Rules from Databases: A Rough Set Approach.
ICDE 1996: 96-105
BibTeX
ACM SIGMOD Anthology - DBLP:
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