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ML
2006
ACM

Propositionalization-based relational subgroup discovery with RSD

13 years 11 months ago
Propositionalization-based relational subgroup discovery with RSD
Abstract Relational rule learning algorithms are typically designed to construct classification and prediction rules. However, relational rule learning can be adapted also to subgroup discovery. This paper proposes a propositionalization approach to relational subgroup discovery, achieved through appropriately adapting rule learning and first-order feature construction. The proposed approach was successfully applied to standard ILP problems (East-West trains, King-Rook-King chess endgame and mutagenicity prediction) and two real-life problems (analysis of telephone calls and traffic accident analysis). Keywords Relational data mining . Propositionalization . Feature construction . Subgroup discovery
Filip Zelezný, Nada Lavrac
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where ML
Authors Filip Zelezný, Nada Lavrac
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