An Attribute-Oriented Rough Set Approach for Knowledge Discovery in Databases

In this paper we present an attribute-oriented rough set approach for knowledge discovery in databases. The method integrates machine learning paradigm, especially learning-from-examples techniques, with rough-set techniques. An attribute-oriented concept tree ascension technique is first applied in generalization, which substantially reduces the computational complexity of database learning processes. Then the cause-effect relationship among the attributes in the database is analyzed using rough set techniques and the unimportant or irrelevant attributes are eliminated. Thus concise and strong rules with little or no redundant information can be learned efficiently. Our study shows that attribute-oriented induction combined with the rough set technique provides an efficient and effective mechanism for knowledge discovery in database systems.