Abstract
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.
Published Version
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