Add DES to readme

Desbordante · Jan 10, 2025 · b93e464 · b93e464
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diff --git a/README.md b/README.md
@@ -42,6 +42,7 @@ The currently supported data patterns are:
    - Exact unique column combination (discovery and validation)
    - Approximate unique column combination, with $g_1$ metric (discovery and validation)
 * Association rules (discovery)
+* Numerical association rules (discovery)
 * Matching dependencies (discovery)
 * Variable heterogeneous denial constraints (validation)
 
@@ -219,6 +220,9 @@ Here is a list of papers about patterns, organized in the recommended reading or
    - [Sebastian Kruse and Felix Naumann. 2018. Efficient discovery of approximate dependencies. Proc. VLDB Endow. 11, 7 (March 2018), 759–772.](https://www.vldb.org/pvldb/vol11/p759-kruse.pdf)
 * Association rules
    - [Charu C. Aggarwal, Jiawei Han. 2014. Frequent Pattern Mining. Springer Cham. pp 471.](https://link.springer.com/book/10.1007/978-3-319-07821-2)
+* Numerical association rules
+   - [Minakshi Kaushik, Rahul Sharma, Iztok Fister Jr., and Dirk Draheim. 2023. Numerical Association Rule Mining: A Systematic Literature Review. 1, 1 (July 2023), 50 pages.](https://arxiv.org/abs/2307.00662)
+   - [Fister, Iztok & Fister jr, Iztok. 2020. uARMSolver: A framework for Association Rule Mining. 10.48550/arXiv.2010.10884.](https://doi.org/10.48550/arXiv.2010.10884)
 * Matching dependencies
    - [Philipp Schirmer, Thorsten Papenbrock, Ioannis Koumarelas, and Felix Naumann. 2020. Efficient Discovery of Matching Dependencies. ACM Trans. Database Syst. 45, 3, Article 13 (September 2020), 33 pages. https://doi.org/10.1145/3392778](https://dl.acm.org/doi/10.1145/3392778)
 * Denial constraints

diff --git a/README_PYPI.md b/README_PYPI.md
@@ -60,6 +60,7 @@ The currently supported data patterns are:
    - Exact unique column combination (discovery and validation)
    - Approximate unique column combination, with $g_1$ metric (discovery and validation)
 * Association rules (discovery)
+* Numerical association rules (discovery)
 * Matching dependencies (discovery)
 * Variable heterogeneous denial constraints (validation)
 
@@ -220,6 +221,9 @@ Here is a list of papers about patterns, organized in the recommended reading or
    - [Sebastian Kruse and Felix Naumann. 2018. Efficient discovery of approximate dependencies. Proc. VLDB Endow. 11, 7 (March 2018), 759–772.](https://www.vldb.org/pvldb/vol11/p759-kruse.pdf)
 * Association rules
    - [Charu C. Aggarwal, Jiawei Han. 2014. Frequent Pattern Mining. Springer Cham. pp 471.](https://link.springer.com/book/10.1007/978-3-319-07821-2)
+* Numerical association rules
+   - [Minakshi Kaushik, Rahul Sharma, Iztok Fister Jr., and Dirk Draheim. 2023. Numerical Association Rule Mining: A Systematic Literature Review. 1, 1 (July 2023), 50 pages.](https://arxiv.org/abs/2307.00662)
+   - [Fister, Iztok & Fister jr, Iztok. 2020. uARMSolver: A framework for Association Rule Mining. 10.48550/arXiv.2010.10884.](https://doi.org/10.48550/arXiv.2010.10884)
 * Matching dependencies
    - [Philipp Schirmer, Thorsten Papenbrock, Ioannis Koumarelas, and Felix Naumann. 2020. Efficient Discovery of Matching Dependencies. ACM Trans. Database Syst. 45, 3, Article 13 (September 2020), 33 pages. https://doi.org/10.1145/3392778](https://dl.acm.org/doi/10.1145/3392778)
 * Denial constraints

diff --git a/examples/basic/README.md b/examples/basic/README.md
@@ -9,6 +9,7 @@ These scenarios showcase a single pattern by discussing its definition and provi
 + [mining_ac.py](https://github.com/Desbordante/desbordante-core/tree/main/examples/basic/mining_ac.py) — a scenario showing the discovery of algebraic constraints.
 + [mining_afd.py](https://github.com/Desbordante/desbordante-core/tree/main/examples/basic/mining_afd.py) — a scenario showing how to discover approximate functional dependencies.
 + [mining_ar.py](https://github.com/Desbordante/desbordante-core/tree/main/examples/basic/mining_ar.py) — a scenario showing how to discover association rules.
++ [mining_nar.py](https://github.com/Desbordante/desbordante-core/tree/main/examples/basic/mining_nar.py) — a scenario showing how to discover numerical association rules.
 + [mining_aucc.py](https://github.com/Desbordante/desbordante-core/tree/main/examples/basic/mining_aucc.py) — a scenario showing how to discover approximate unique column combinations.
 + [mining_cfd.py](https://github.com/Desbordante/desbordante-core/tree/main/examples/basic/mining_cfd.py) — a scenario showing how to discover conditional functional dependencies.
 + [mining_dd.py](https://github.com/Desbordante/desbordante-core/tree/main/examples/basic/mining_dd.py) — a scenario showing how to discover differential dependencies.