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..
A Symbolic Approach to Computing Disjunctive Association Rules from Data
Authors: Said Jabbour, Badran Raddaoui, Lakhdar Sais
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we show through an extensive campaign of experiments on several popular real-life datasets the efficiency of our proposed approach. We conduct extensive experiments on different popular real-world datasets to evaluate the efficiency of our approach to discover (k, k )-disjunctive support based association rules in sequential and parallel setting. |
| Researcher Affiliation | Academia | Said Jabbour1 , Badran Raddaoui2,3 and Lakhdar Sais1 1CRIL, Universit e d Artois & CNRS, France 2SAMOVAR, T el ecom Sud Paris, Institut Polytechnique de Paris, France 3Institute for Philosophy II, Ruhr University Bochum, Germany |
| Pseudocode | No | While Figure 1 presents a 'SAT Encoding Scheme', it is not explicitly labeled as pseudocode or an algorithm, nor does it follow a typical pseudocode format. |
| Open Source Code | No | The paper mentions that its approach is 'implemented in the C++ language top-on the well-known satisfiability solver Mini SAT', but it does not provide any statement or link indicating that its own implementation code is open-source or publicly available. |
| Open Datasets | Yes | We use a set of datasets coming from the FIMI2 repository. 2http://fimi.ua.ac.be/data/ |
| Dataset Splits | No | The paper mentions using datasets for experiments but does not specify any explicit train/validation/test splits, percentages, or other detailed splitting methodologies. |
| Hardware Specification | Yes | Our experiments were performed on a Linux machine with Intel Xeon quad-core processors and 32GB of RAM running at 2.66 GHz. |
| Software Dependencies | No | The paper mentions that its approach is implemented in 'C++ language' and uses the 'satisfiability solver Mini SAT [E en and S orensson, 2002]' and 'Open MP' for parallelization. However, it does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | For all runs, time-out and memory-out were set to 2 hours and 10 GB, respectively. We also fix the minimum confidence threshold β to 95%3 while the value of γ is identical to α. |