Constraint Programming for Mining Borders of Frequent Itemsets

Authors: Mohamed-Bachir Belaid, Christian Bessiere, Nadjib Lazaar

IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We made several experiments to compare our CP model MODELD,s,b to the state of the art approaches. ... For each approach and each selected instance, Table 2 reports the CPU time and the number of MFIs/MIIs.
Researcher Affiliation Academia Mohamed-Bachir Belaid , Christian Bessiere and Nadjib Lazaar LIRMM, University of Montpellier, CNRS, Montpellier, France {belaid, bessiere, lazaar}@lirmm.fr
Pseudocode Yes Algorithm 1: Propagator for FREQUENTSUBS and Algorithm 2: Propagator for INFREQUENTSUPERS
Open Source Code Yes The code is publicly available at gite.lirmm.fr/belaid/cp4borders.
Open Datasets Yes We selected several real-sized datasets from the FIMI repository (fimi.ua.ac.be/data).
Dataset Splits No The paper refers to a 'transactional dataset' and a 'support threshold', but does not explicitly provide details about training, validation, or test dataset splits, cross-validation, or sample counts for reproduction.
Hardware Specification Yes All experiments were conducted on an Intel core i7, 2.2Ghz with a RAM of 8Gb and a timeout of one hour.
Software Dependencies No The paper mentions that the implementation was carried out 'in the Oscar solver using Scala' but does not provide specific version numbers for either Oscar or Scala.
Experiment Setup Yes After a few preliminary tests, we decided to use smallest item frequency first as variable ordering heuristic and largest value first as value ordering heuristic. We used the global constraint COVERSIZE to encode the constraint (3) in MODELD,s,b.