From 3395d124c5c01c6089860af74465c6e6427f2437 Mon Sep 17 00:00:00 2001 From: Ivan Volgushev Date: Tue, 7 Jan 2025 20:51:49 +0300 Subject: [PATCH] Add nar to readme --- README.md | 3 +++ examples/basic/README.md | 1 + 2 files changed, 4 insertions(+) diff --git a/README.md b/README.md index 375a2153fd..d68a5aac98 100644 --- a/README.md +++ b/README.md @@ -36,6 +36,7 @@ The currently supported data patterns are: - set-based axiomatization (discovery) - list-based axiomatization (discovery) * Metric functional dependencies (validation) +* Numerical association rules (discovery) * Fuzzy algebraic constraints (discovery) * Differential Dependencies (discovery) * Unique column combinations: @@ -219,6 +220,8 @@ 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) * 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..093d24b6cd 100644 --- a/examples/basic/README.md +++ b/examples/basic/README.md @@ -16,6 +16,7 @@ These scenarios showcase a single pattern by discussing its definition and provi + [mining_ind.py](https://github.com/Desbordante/desbordante-core/tree/main/examples/basic/mining_ind.py) — a scenario showing how to discover inclusion dependencies. + [mining_list_od.py](https://github.com/Desbordante/desbordante-core/tree/main/examples/basic/mining_list_od.py) — a scenario showing how to discover order dependencies based on list axiomatization. + [mining_md.py](https://github.com/Desbordante/desbordante-core/tree/main/examples/basic/mining_md.py) — a scenario showing how to discover matching dependencies. ++ [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_pfd.py](https://github.com/Desbordante/desbordante-core/tree/main/examples/basic/mining_pfd.py) — a scenario showing how to discover probabilistic functional dependencies. + [mining_set_od_1.py](https://github.com/Desbordante/desbordante-core/tree/main/examples/basic/mining_set_od_1.py) — a scenario showing how to discover order dependencies based on set axiomatization, part 1. + [mining_set_od_2.py](https://github.com/Desbordante/desbordante-core/tree/main/examples/basic/mining_set_od_2.py) — a scenario showing how to discover order dependencies based on set axiomatization, part 2.