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SeerCuts: Explainable Attribute Discretization

Summary: SeerCuts delivers explainable discretization for numerical attributes, aligning partitions with a task utility. Outputs bins balancing utility and interpretability, turning age into decade or life-stage bins for interpretable features. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
7243
Venue
SIGMOD
Year
2025
Pagerank
5.093636e-05
Overall Rank
10,735 | 26.35%
DOI
10.1145/3722212.3725132

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Authors

BibTeX Citation

@inproceedings{lai_sigmod25,
        title = {{SeerCuts: Explainable Attribute Discretization}},
        author = {Lai, Eugenie Y and Croitoru, Inbal and Bitton, Noam and Shalem, Ariel and Youngmann, Brit and Galhotra, Sainyam and Rezig, El Kindi and Cafarella, Michael},
        series = {{SIGMOD} '25},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3722212.3725132},
        url = {https://dl.acm.org/doi/10.1145/3722212.3725132},
        year = {2025}
}

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
410 SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics 2015 VLDB 0.0001890421
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