Two-phase rule induction from incomplete data

A framework of learning a new form of rules from incomplete data is introduced so that a user can easily identify attributes with or without missing values in a rule. Two levels of measurement are assigned to a rule. An algorithm for two-phase rule induction is presented. Instead of filling in missing attribute values before or during the process of rule induction, we divide rule induction into two phases. In the first phase, rules and partial rules are induced based on non-missing values. In the second phase, partial rules are modified and refined by filling in some missing values. Such rules truthfully reflect the knowledge embedded in the incomplete data. The study not only presents a new view of rule induction from incomplete data, but also provides a practical solution.