Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Targeting Minimal Rare Itemsets from Transaction Databases
Authors: Amel Hidouri, Badran Raddaoui, Said Jabbour
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, to evaluate the effectiveness and efficiency of our approach, we conduct extensive experimental analysis using various popular datasets. |
| Researcher Affiliation | Academia | 1 CRIL & CNRS, Universite d Artois, Lens, France 2 SAMOVAR, T el ecom Sud Paris, Institut Polytechnique de Paris, France 3Institute for Philosophy II, Ruhr University Bochum, Germany |
| Pseudocode | Yes | Algorithm 1 summarizes our SAT-based approach for mining the set of 1-MRIs from transaction databases. |
| Open Source Code | Yes | Our source code and datasets are available at https://github.com/ amel-hidouri/SAMRIC.git. |
| Open Datasets | Yes | For our empirical evaluation, experiments were carried out on different commonly used benchmark datasets taken from the well-known repositories FIMI1, CP4IM2 and SPMF3. 1http://fimi.ua.ac.be/data/ 2http://dtai.cs.kuleuven.be/CP4IM/datasets/ 3https://www.philippe-fournier-viger.com/spmf/index.php? link=datasets.php |
| Dataset Splits | No | No specific train/validation/test splits are mentioned. The paper describes the use of entire benchmark datasets for mining itemsets, which does not typically involve explicit data splitting for training/validation/testing in the traditional machine learning sense. |
| Hardware Specification | Yes | Our experiments were performed on a Linux machine 32GB of RAM running at 2.66 GHz. |
| Software Dependencies | No | Algorithm 1 uses the Mini SAT solver, which is a popular SAT solver written in C++. No specific version numbers for the Mini SAT solver or C++ are provided, which are necessary for reproducible software dependencies. |
| Experiment Setup | Yes | We test our approach for k = {1, 2} while varying the minimum support threshold (λ). |