Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery

Mario Boley, Bryan R. Goldsmith, Luca M. Ghiringhelli, Jilles Vreeken

Research output: Contribution to journalArticleResearchpeer-review

16 Citations (Scopus)


Existing algorithms for subgroup discovery with numerical targets do not optimize the error or target variable dispersion of the groups they find. This often leads to unreliable or inconsistent statements about the data, rendering practical applications, especially in scientific domains, futile. Therefore, we here extend the optimistic estimator framework for optimal subgroup discovery to a new class of objective functions: we show how tight estimators can be computed efficiently for all functions that are determined by subgroup size (non-decreasing dependence), the subgroup median value, and a dispersion measure around the median (non-increasing dependence). In the important special case when dispersion is measured using the mean absolute deviation from the median, this novel approach yields a linear time algorithm. Empirical evaluation on a wide range of datasets shows that, when used within branch-and-bound search, this approach is highly efficient and indeed discovers subgroups with much smaller errors.

Original languageEnglish
Pages (from-to)1391-1418
Number of pages28
JournalData Mining and Knowledge Discovery
Issue number5
Publication statusPublished - Sep 2017
Externally publishedYes


  • Branch-and-bound search
  • Local pattern discovery
  • Subgroup discovery

Cite this