TY - JOUR
T1 - Data-Driven Discovery of Quantitative Rules in Relational Databases
AU - Han, Jiawei
AU - Cai, Yandong
AU - Cercone, Nick
N1 - Funding Information:
Manuscript received December 19, 1989; revised September 18, 1990. This work was supported in part by the Natural Sciences and Research Council of Canada under Operating Grants A-3723 and A-4309 and by a research grant from the Centre for Systems Science, Simon Fraser University. The authors are with the School of Computing Science, Simon Fraser University, Burnaby, B.C., Canada V5A 1S6. IEEE Log Number 9205833.
PY - 1993/2
Y1 - 1993/2
N2 - A quantitative rule is a rule associated with quantitative information which assesses the representativeness of the rule in the database. In this paper, an efficient induction method is developed 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. More-over, 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.
AB - A quantitative rule is a rule associated with quantitative information which assesses the representativeness of the rule in the database. In this paper, an efficient induction method is developed 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. More-over, 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.
KW - Knowledge discovery in databases
KW - attribute-oriented induction
KW - characteristic rules
KW - classification rules
KW - data-driven learning algorithms
KW - machine learning
KW - quantitative rules
UR - http://www.scopus.com/inward/record.url?scp=0027542839&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0027542839&partnerID=8YFLogxK
U2 - 10.1109/69.204089
DO - 10.1109/69.204089
M3 - Article
AN - SCOPUS:0027542839
SN - 1041-4347
VL - 5
SP - 29
EP - 40
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 1
ER -