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Outlier Summarization via Human Interpretable Rules

Summary: STAIR summarizes outliers with concise, value-aware human-interpretable rules, distinguishing causes within the same subspace via interpretation-aware optimization. L-STAIR adds locality, jointly partitioning complex high-dimensional data and learning localized rule sets. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
hc7e8d0fcf375b0c4
Venue
VLDB
Year
2024
Pagerank
5.425954e-05
Overall Rank
7,924 | 46.73%
DOI
10.14778/3654621.3654627

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{deng_vldb24,
        title = {{Outlier Summarization via Human Interpretable Rules}},
        author = {Deng, Yuhao and Wang, Yu and Cao, Lei and Qiao, Lianpeng and Wang, Yuping and Xu, Jingzhe and Yan, Yizhou and Madden, Samuel},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {7},
        pages = {1591--1604},
        doi = {10.14778/3654621.3654627},
        url = {https://doi.org/10.14778/3654621.3654627},
        year = {2024}
}

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