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Generalized Knowledge Discovery from Relational Databases


Yu-Ying Wu, Yen-Liang Chen, Ray-I Chang


Vol. 9  No. 6  pp. 148-153


The attribute-oriented induction (AOI) method is a useful tool for data capable of extracting generalized knowledge from relational data and the user’s background knowledge. However, a potential weakness of AOI is that it only provides a snapshot of generalized knowledge, not a global picture. In addition, the method only mined knowledge from positive facts in databases. Rare but important negative generalized knowledge can be missed. Hence, the aim of this study is to proposal two novel mining approaches to generate multiple-level positive and negative generalized knowledge. The approaches discussed in this paper are more flexible and powerful than currently utilized methods and can be expected to have wide applications in diverse areas including e-commerce, e-learning, library science, and so on.


Data mining, Attribute-oriented Induction, Knowledge discovery, Multiple-level mining, Negative pattern