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 |
|---|---|---|---|---|
| 10,513 | Subgroup Discovery with Small and Alternative Feature Sets | 2025 | SIGMOD | 4.1905499e-05 |
| 11,253 | Fast Search-By-Classification for Large-Scale Databases Using Index-Aware Decision Trees and Random Forests | 2023 | VLDB | 4.1905499e-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 | 0.0010863639 |
| 182 | Mining Frequent Patterns without Candidate Generation | 2000 | SIGMOD | 0.00036955562 |
| 602 | Mining Quantitative Association Rules in Large Relational Tables | 1996 | SIGMOD | 0.00019350521 |
| 651 | Dynamic Itemset Counting and Implication Rules for Market Basket Data | 1997 | SIGMOD | 0.00018649942 |
| 1,096 | Interpretable and Informative Explanations of Outcomes | 2015 | VLDB | 0.00014088686 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,784 | Ratio Rules: A New Paradigm for Fast, Quantifiable Data Mining | 1998 | VLDB | 4.9216736e-05 |
| 10,881 | RED: Effective Trajectory Representation Learning with Comprehensive Information | 2025 | VLDB | 4.1905499e-05 |
| 10,499 | Incremental Rule Discovery in Response to Parameter Updates | 2025 | SIGMOD | 4.1905499e-05 |
| 9,847 | Discovering Top-k Relevant and Diversified Rules | 2024 | SIGMOD | 4.2680295e-05 |
| 11,042 | Efficient Discovery of Significant Patterns with Few-Shot Resampling | 2024 | VLDB | 4.1905499e-05 |
| 9,158 | Computing Rule-Based Explanations by Leveraging Counterfactuals | 2023 | VLDB | 4.380727e-05 |
| 10,513 | Subgroup Discovery with Small and Alternative Feature Sets | 2025 | SIGMOD | 4.1905499e-05 |
| 7,283 | Discovering Association Rules from Big Graphs | 2022 | VLDB | 4.7716465e-05 |
| 9,362 | Discovering Top-k Rules using Subjective and Objective Criteria | 2023 | SIGMOD | 4.3472627e-05 |
| 9,962 | Parallel Rule Discovery from Large Datasets by Sampling | 2022 | SIGMOD | 4.2254157e-05 |