Abstract
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 |
---|---|
Original language | English |
Title of host publication | Proceedings of the 12th International Conference on Inductive Logic Programming |
Pages | 149-165 |
Publication status | Published - 2002 |
Bibliographical note
ISBN: 3540005676Publisher: Springer-Verlag
Name and Venue of Conference: Proceedings of the 12th International Conference on Inductive Logic Programming
Other identifier: 2000552