Mining the Minoria: Unknown, Under-represented, and Under-performing Minority Groups
Summary: Propose “minority mining”: detect unknown, under‑represented and under‑performing subgroups from attribute‑only data by mapping to a dual space and exploiting hyperplane‑arrangement geometry. Provide an efficient low‑dim algorithm, a search‑based high‑dim heuristic to beat the curse of dimensionality, with theoretical analysis and empirical validation. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,223 | On Fair Epsilon Net and Geometric Hitting Set | 2026 | VLDB | 4.1905499e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,942 | SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging | 2021 | SIGMOD | 0.00010010569 |
| 2,477 | Computing k-Regret Minimizing Sets | 2014 | VLDB | 8.6907684e-05 |
| 3,170 | Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence | 2021 | SIGMOD | 7.4517805e-05 |
| 4,746 | Slice Tuner: A Selective Data Acquisition Framework for Accurate and Fair Machine Learning Models | 2021 | SIGMOD | 5.9446518e-05 |
| 5,558 | On Obtaining Stable Rankings | 2019 | VLDB | 5.4375292e-05 |
| 6,804 | RRR: Rank-Regret Representative | 2019 | SIGMOD | 4.917138e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,528 | Algorithms for Mining Association Rules for Binary Segmentations of Huge Categorical Databases | 1998 | VLDB | 6.1063994e-05 |
| 4,761 | Automated Feature Engineering for Algorithmic Fairness | 2021 | VLDB | 5.9341687e-05 |
| 148 | On the Design and Quantification of Privacy Preserving Data Mining Algorithms | 2001 | PODS | 0.00041196325 |
| 6,460 | Data mining, Hypergraph Transversals, and Machine Learning | 1997 | PODS | 5.0481624e-05 |
| 6,462 | Tailoring Data Source Distributions for Fairness-aware Data Integration | 2021 | VLDB | 5.0479645e-05 |
| 11,450 | Grouped Learning: Group-By Model Selection Workloads | 2021 | SIGMOD | 4.1905499e-05 |
| 7,605 | Causal Feature Selection for Algorithmic Fairness | 2022 | SIGMOD | 4.6943015e-05 |
| 4,866 | OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning | 2021 | SIGMOD | 5.8620848e-05 |
| 559 | Maintaining Data Privacy in Association Rule Mining | 2002 | VLDB | 0.0002012549 |
| 40 | Privacy-Preserving Data Mining | 2000 | SIGMOD | 0.00074213516 |