RSD: Relational Subgroup Discovery through First-Order Feature Construction

Lavrac Nada, Zelezny Filip, Peter Flach

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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 contributionRSD: Relational Subgroup Discovery through First-Order Feature Construction
Original languageEnglish
Title of host publicationProceedings of the 12th International Conference on Inductive Logic Programming
Pages149-165
Publication statusPublished - 2002

Bibliographical note

ISBN: 3540005676
Publisher: Springer-Verlag
Name and Venue of Conference: Proceedings of the 12th International Conference on Inductive Logic Programming
Other identifier: 2000552

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