TY - JOUR
T1 - Towards efficient induction mechanisms in database systems
AU - Han, Jiawei
N1 - Correspondence to: J. Han, School of Computing Science, Simon Fraser University, Burnaby, B.C., V5A lS6, Canada. Email: [email protected]. *The research was supported in part by grants from the Natural Sciences and Engineering Research Council of Canada and the Centre for Systems Science of Simon Fraser University. This paper is a substantially extended and revised version of one section of the article. Efficient deduction and induction: key to the success of data-intensive knowledge-base systems, appearing in Formal Methods in Databases and Software Engineering, Workshops in Computing (Springer, London, 1993).
PY - 1994/10/24
Y1 - 1994/10/24
N2 - With the wide availability of huge amounts of data in database systems, the extraction of knowledge in databases by efficient and powerful induction or knowledge discovery mechanisms has become an important issue in the construction of new generation database and knowledge-base systems. In this article, an attribute-oriented induction method for knowledge discovery in databases is investigated, which provides an efficient, set-oriented induction mechanism for extraction of different kinds of knowledge rules, such as characteristic rules, discriminant rules, data evolution regularities and high level dependency rules in large relational databases. Our study shows that the method is robust in the existence of noise and database updates, is extensible to knowledge discovery in advanced and/or special purpose databases, such as object-oriented databases, active databases, spatial databases, etc., and has wide applications.
AB - With the wide availability of huge amounts of data in database systems, the extraction of knowledge in databases by efficient and powerful induction or knowledge discovery mechanisms has become an important issue in the construction of new generation database and knowledge-base systems. In this article, an attribute-oriented induction method for knowledge discovery in databases is investigated, which provides an efficient, set-oriented induction mechanism for extraction of different kinds of knowledge rules, such as characteristic rules, discriminant rules, data evolution regularities and high level dependency rules in large relational databases. Our study shows that the method is robust in the existence of noise and database updates, is extensible to knowledge discovery in advanced and/or special purpose databases, such as object-oriented databases, active databases, spatial databases, etc., and has wide applications.
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U2 - 10.1016/0304-3975(94)90194-5
DO - 10.1016/0304-3975(94)90194-5
M3 - Article
AN - SCOPUS:0028516602
SN - 0304-3975
VL - 133
SP - 361
EP - 385
JO - Theoretical Computer Science
JF - Theoretical Computer Science
IS - 2
ER -