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)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Eugenie Y Lai
- 2. Inbal Croitoru
- 3. Noam Bitton
- 4. Ariel Shalem
- 5. Brit Youngmann
- 6. Sainyam Galhotra
- 7. El Kindi Rezig
- 8. Michael Cafarella
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 460 | SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics | 2015 | VLDB | 0.00022516069 |
Previous
Page 1 / 1
Next