An attribute-oriented approach for learning classification rules from relational databases

A classification rule is a rule which characterizes the properties that distinguish one class from other classes. An attribute-oriented induction algorithm which extracts classification rules from relational databases is developed. The algorithm adopts the artificial intelligence learning from examples paradigm and applies an attribute-oriented concept tree ascending technique in the learning process. The technique integrates database operations with the learning process and provides a simple and efficient way of learning from large databases. The algorithm learns both conjunctive rules and restricted forms of disjunctive rules. Using database statistics, learning can be performed on databases containing noisy data and exceptions. An analysis and comparison with other algorithms show that attribute-oriented induction substantially reduces the complexity of database learning processes