Targeting Minimal Rare Itemsets from Transaction Databases
Authors: Amel Hidouri, Badran Raddaoui, Said Jabbour
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | 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). |