# Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery

arXiv: https://arxiv.org/abs/1701.07696 · HTML: https://mlsanity.com/paper/1701.07696/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2017.

## Built on by (most cited first, 7 of 7)

- [Better Short than Greedy: Interpretable Models through Optimal Rule Boosting](https://mlsanity.com/paper/2101.08380/) (2021)
- [Learning Exceptional Subgroups by End-to-End Maximizing KL-divergence](https://mlsanity.com/paper/2402.12930/) (2024)
- [Robust subgroup discovery](https://mlsanity.com/paper/2103.13686/) (2021)
- [Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles](https://mlsanity.com/paper/2402.15691/) (2024)
- [Mining Java Memory Errors using Subjective Interesting Subgroups with Hierarchical Targets](https://mlsanity.com/paper/2310.00781/) (2023)
- [Local Exceptionality Detection in Time Series Using Subgroup Discovery](https://mlsanity.com/paper/2108.11751/) (2021)
- [Discovering Subgroups with Exceptional Survival Characteristics](https://mlsanity.com/paper/2602.22179/) (2026)

## Abstract (arXiv)

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 average 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.