A Symbolic Approach to Computing Disjunctive Association Rules from Data

Authors: Said Jabbour, Badran Raddaoui, Lakhdar Sais

IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | 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.