Handling exceptions in automatically generated knowledge bases

Introduces the idea of using nonmonotonic inheritance networks for the storage and maintenance of knowledge discovered in data. While existing data mining strategies for knowledge discovery in data typically involve initial structuring through the use of identification trees (IDTs) and the subsequent extraction of rules from these trees for use in rule-based expert systems, such strategies have difficulty in coping with additional information which may conflict with that already used for the automatic generation of rules. In the worst case, the entire automatic sequence may have to be repeated. If nonmonotonic inheritance networks are used instead of rules for storing knowledge discovered in data, additional conflicting information can be inserted directly into such networks as exceptions, thereby by-passing the need for recompilation