Machine Learning

, Volume 62, Issue 1–2, pp 33–63 | Cite as

Propositionalization-based relational subgroup discovery with RSD

  • Filip ŽeleznýEmail author
  • Nada Lavrač


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).


Relational data mining Propositionalization Feature construction Subgroup discovery 


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Copyright information

© Springer Science + Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Czech Technical UniversityPragueCzech Republic
  2. 2.Institute Jožef StefanLjubljana, Slovenia, and Nova Gorica PolytechnicNova GoricaSlovenia

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