ML Anthology

Propositionalization-Based Relational Subgroup Discovery with RSD

Filip Zelezný, Nada Lavrac
MLJ 2006 pp. 33-63
doi:10.1007/S10994-006-5834-0 /mlj/2006/zelezny2006mlj-propositionalizationbased/

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).

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Text

Zelezný and Lavrac. "Propositionalization-Based Relational Subgroup Discovery with RSD." Machine Learning, 2006. doi:10.1007/S10994-006-5834-0

Markdown

[Zelezný and Lavrac. "Propositionalization-Based Relational Subgroup Discovery with RSD." Machine Learning, 2006.](https://mlanthology.org/mlj/2006/zelezny2006mlj-propositionalizationbased/) doi:10.1007/S10994-006-5834-0

BibTeX

@article{zelezny2006mlj-propositionalizationbased,
  title     = {{Propositionalization-Based Relational Subgroup Discovery with RSD}},
  author    = {Zelezný, Filip and Lavrac, Nada},
  journal   = {Machine Learning},
  year      = {2006},
  pages     = {33-63},
  doi       = {10.1007/S10994-006-5834-0},
  volume    = {62},
  url       = {https://mlanthology.org/mlj/2006/zelezny2006mlj-propositionalizationbased/}
}