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)

Paper ID: 14000
Venue: VLDB
Year: 2025
Pagerank: 5.093636e-05
Overall Rank: 10,823 | 25.75%
DOI: 10.14778/3718057.3718072

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Mohsen Dehghankar (University of Illinois Chicago)
2. Abolfazl Asudeh (University of Illinois Chicago)

BibTeX Citation

@article{dehghankar_vldb25,
        title = {{Mining the Minoria: Unknown, Under-represented, and Under-performing Minority Groups}},
        author = {Dehghankar, Mohsen and Asudeh, Abolfazl},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {5},
        pages = {1453--1465},
        doi = {10.14778/3718057.3718072},
        url = {https://doi.org/10.14778/3718057.3718072},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,511	On Fair Epsilon Net and Geometric Hitting Set	2026	VLDB	5.093636e-05

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
2,063	Computing k-Regret Minimizing Sets	2014	VLDB	9.2399169e-05
2,273	SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging	2021	SIGMOD	8.8230899e-05
3,190	Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence	2021	SIGMOD	7.6532441e-05
4,382	Slice Tuner: A Selective Data Acquisition Framework for Accurate and Fair Machine Learning Models	2021	SIGMOD	6.7331832e-05
5,465	On Obtaining Stable Rankings	2019	VLDB	6.2075408e-05
6,592	RRR: Rank-Regret Representative	2019	SIGMOD	5.830041e-05

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