Abstract

Discovering decision trees is an important set of techniques in KDD, both because of their simple interpretation and the efficiency of their discovery. One of their disadvantages is that they do not take the structure of the mining object 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 generalization loses the efficiency of the standard algorithms. In this paper we present a framework that allows the efficient discovery of multi-relational decision trees through the exploitation of the domain knowledge encoded in the data model of the database. Introduction The induction of decision trees has been getting a lot of attention in the field of Knowledge Discovery in Databases over the past few years. This popularity has been largely due to the efficiency with which decision trees can be induced from large datasets, as well as to the elegant and intuitive representation ...

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