Efficiently Finding Conceptual Clustering Models with Integer Linear Programming
Authors: Abdelkader Ouali, Samir Loudni, Yahia Lebbah, Patrice Boizumault, Albrecht Zimmermann, Lakhdar Loukil
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | 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. |