Relational rule learning is typically used in solving classi- fication and prediction tasks. However, relational rule learning can be adapted also to subgroup discovery. This paper proposes a proposition- alization approach to relational subgroup discovery, achieved through appropriately adapting rule learning and first-order feature construction. The proposed approach, applicable to subgroup discovery in individual- centered domains, was successfully applied to two standard ILP problems (East-West trains and KRK) and a real-life telecommunications applica- tion.
|Translated title of the contribution||RSD: Relational Subgroup Discovery through First-Order Feature Construction|
|Title of host publication||Proceedings of the 12th International Conference on Inductive Logic Programming|
|Publication status||Published - 2002|
Bibliographical noteISBN: 3540005676
Name and Venue of Conference: Proceedings of the 12th International Conference on Inductive Logic Programming
Other identifier: 2000552