PY-SHARQ: A Holistic Python Library for Explaining Association Rules on Relational Data
Summary: Demo of PY-SHARQ, a Python library for explaining association rules on relational data, using SHARQ (Shapley-based element contribution) to quantify influence in a rule set. Enables element-, rule-, and attribute-level importance analyses, addressing gaps beyond ITop/Infl. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Hadar Ben-Efraim
- 2. Susan B. Davidson
- 3. Amit Somech
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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 |
|---|---|---|---|---|
| 36 | Fast Algorithms for Mining Association Rules | 1994 | VLDB | 0.00076114894 |
| 213 | Scorpion: Explaining Away Outliers in Aggregate Queries | 2013 | VLDB | 0.0003371037 |
| 10,404 | SHARQ: Explainability Framework for Association Rules on Relational Data | 2025 | SIGMOD | 4.1905499e-05 |
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