Tight Optimistic Estimates for Fast Subgroup Discovery

@inproceedings{Grosskreutz2008TightOE,
  title={Tight Optimistic Estimates for Fast Subgroup Discovery},
  author={Henrik Grosskreutz and Stefan R{\"u}ping and Stefan Wrobel},
  booktitle={ECML/PKDD},
  year={2008}
}
Subgroup discovery is the task of finding subgroups of a population which exhibit both distributional unusualness and high generality. Due to the non monotonicity of the corresponding evaluation functions, standard pruning techniques cannot be used for subgroup discovery, requiring the use of optimistic estimate techniques instead. So far, however, optimistic estimate pruning has only been considered for the extremely simple case of a binary target attribute and up to now no attempt was made to… CONTINUE READING
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Optimistic estimate pruning strategies for fast exhaustive subgroup discovery

H. Grosskreutz, S. Rüping, N. Shaabani, S. Wrobel
Technical report, Fraunhofer Institute IAIS • 2008

Convex Optimization

S. Boyd, L. Vandenberghe
2004

Flach . Decision support through subgroup discovery : Three case studies and the lessons learned

Bojan Cestnik Nada Lavrac, Dragan Gamberger
Machine Learning • 2002

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