Discovering decision trees is an important set of techniques in KDD, both because of their simple interpretation and the efficiency of their discovery. One disadvantage is that they do not take the structure of the data into account. By going from the standard single-relation approach to the multi-relational approach as in ILP this disadvantage is removed. However, the straightforward generalisation loses the efficiency. In this paper we present a framework that allows for efficient discovery of multi-relational decision trees through exploitation of domain knowledge encoded in the data model of the database.
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