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Induction of Abstraction Operators using Unsupervised Discretization of Continuous Attributes

Most complex real-world domains are described by both relations among objects and numeric features. While ILP allows to handle relations, it is less suited to deal with numeric values. Abstraction operators can help in these cases by replacing specific values with intervals. Since it is difficult for humans to provide useful discretizations, we propose a method for automatically learning the abstraction operator from available observations. The resulting abstraction theories are then tested on a well-known dataset.