Discovery of decision rules in relational databases

Abstract

We develop an attribute-oriented rough set approach for the discovery of decision rules in relational databases. Our approach combines machine learning techniques and rough set theory. We consider a learning procedure to consist of the two phases data generalization and data reduction. In the data generalization phase, utilizing knowledge about concept hierarchies and relevance of the data, an attribute-oriented induction is performed attribute by attribute. Some undesirable attributes of the discovery task are removed and the primitive data in the databases are generalized to the desirable level; this process greatly decreases the number of tuples which must be examined for the discovery task and substantially reduces the computational complexity of the database learning processes. Subsequently, in data reduction phase, rough set theory is applied to the generalized relation; the cause-effect relationships among the condition and decision attributes in the databases are analyzed and the non-essential or irrelevant attributes to the discovery task are eliminated without losing information of the original database system. This process further reduces the generalized relation. Thus very concise and more accurate decision rules for each class in the decision attribute with little or no redundancy information, can be extracted automatically from the reduced relation during the learning process. Our study shows that attribute-oriented induction combined with rough set theory provide an efficient and effective mechanism for discovering decision rules in database systems.