REDS: Rule Extraction for Discovering Scenarios
Summary: REDS introduces rule extraction for scenario discovery by bootstrapping subgroup discovery with an intermediate ML labeler on few simulations. It reduces simulations by 50–75%, enables semi-supervised discovery, and improves scenario quality on third-party data when a simulator is unavailable. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Vadim Arzamasov
- 2. Klemens Böhm
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,504 | Subgroup Discovery with Small and Alternative Feature Sets | 2025 | SIGMOD | 4.1945683e-05 |
| 11,251 | Fast Search-By-Classification for Large-Scale Databases Using Index-Aware Decision Trees and Random Forests | 2023 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 13 | Mining Association Rules between Sets of Items in Large Databases | 1993 | SIGMOD | 0.0010864752 |
| 181 | Mining Frequent Patterns without Candidate Generation | 2000 | SIGMOD | 0.00036992674 |
| 599 | Mining Quantitative Association Rules in Large Relational Tables | 1996 | SIGMOD | 0.00019394214 |
| 657 | Dynamic Itemset Counting and Implication Rules for Market Basket Data | 1997 | SIGMOD | 0.00018553891 |
| 1,099 | Interpretable and Informative Explanations of Outcomes | 2015 | VLDB | 0.00014096312 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,805 | Ratio Rules: A New Paradigm for Fast, Quantifiable Data Mining | 1998 | VLDB | 4.9222308e-05 |
| 10,877 | RED: Effective Trajectory Representation Learning with Comprehensive Information | 2025 | VLDB | 4.1945683e-05 |
| 9,847 | Discovering Top-k Relevant and Diversified Rules | 2024 | SIGMOD | 4.2721228e-05 |
| 10,489 | Incremental Rule Discovery in Response to Parameter Updates | 2025 | SIGMOD | 4.1945683e-05 |
| 11,039 | Efficient Discovery of Significant Patterns with Few-Shot Resampling | 2024 | VLDB | 4.1945683e-05 |
| 9,158 | Computing Rule-Based Explanations by Leveraging Counterfactuals | 2023 | VLDB | 4.3849295e-05 |
| 10,504 | Subgroup Discovery with Small and Alternative Feature Sets | 2025 | SIGMOD | 4.1945683e-05 |
| 7,287 | Discovering Association Rules from Big Graphs | 2022 | VLDB | 4.7762276e-05 |
| 9,355 | Discovering Top-k Rules using Subjective and Objective Criteria | 2023 | SIGMOD | 4.3514328e-05 |
| 9,963 | Parallel Rule Discovery from Large Datasets by Sampling | 2022 | SIGMOD | 4.2294678e-05 |