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..
Efficiently Finding Conceptual Clustering Models with Integer Linear Programming
Authors: Abdelkader Ouali, Samir Loudni, Yahia Lebbah, Patrice Boizumault, Albrecht Zimmermann, Lakhdar Loukil
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments performed on UCI datasets show that our approach efficiently finds clusterings of consistently high quality. |
| Researcher Affiliation | Academia | Abdelkader Oualia,b, Samir Loudnib, Yahia Lebbaha, Patrice Boizumaultb, Albrecht Zimmermannb and Lakhdar Loukila (a) University of Oran 1 Ahmed Ben Bella, Lab. LITIO, 31000 Oran, Algeria. (b) University of Caen Normandy, CNRS, UMR 6072 GREYC, 14032 Caen, France. |
| Pseudocode | No | The paper provides ILP model formulations in figures (Fig. 1, 2, 3) but these are mathematical problem definitions, not pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a link for a re-implemented third-party tool (CDKMeans) but does not state that the code for their own proposed method (CCLP) is available or open-source. |
| Open Datasets | Yes | Experiments were carried out on the same datasets which were used in [Guns et al., 2013] and available from the UCI repository. |
| Dataset Splits | No | The paper refers to datasets from the UCI repository and lists their characteristics in Table 3, but does not provide specific train/validation/test dataset splits for reproducibility. |
| Hardware Specification | Yes | All experiments were conducted on AMD Opteron 6282SE with 2.60 GHz of CPU and 512 GB of RAM. |
| Software Dependencies | Yes | We used LCM to extract the set of all closed patterns and CPLEX v.12.4 to solve the different ILP models. |
| Experiment Setup | Yes | For all methods, a time limit of 24 hours has been used. Experiments have been performed without any local constraints on individual closed patterns. To evaluate the quality of a clustering, we test the coherence of a clustering, measured by the intra-cluster similarity (ICS) and the inter-clusters dissimilarity (ICD), both of which should be as large as possible. |