From b93e464782f0ef34bc7c39ff98ff316c23901213 Mon Sep 17 00:00:00 2001 From: Ivan Volgushev Date: Fri, 10 Jan 2025 16:07:22 +0300 Subject: [PATCH] Add DES to readme --- README.md | 4 ++++ README_PYPI.md | 4 ++++ examples/basic/README.md | 1 + 3 files changed, 9 insertions(+) diff --git a/README.md b/README.md index 520c630e5e..bf69b8c437 100644 --- 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 index 320849707c..d2227c6379 100644 --- 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 index 848412fe0f..ce3fea1f64 100644 --- 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.