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 Framework for Minimal Clustering Modification via Constraint Programming
Authors: Chia-Tung Kuo, S. Ravi, Thi-Bich-Hanh Dao, Christel Vrain, Ian Davidson1389
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate its usefulness through experiments on social network and medical imaging data sets. |
| Researcher Affiliation | Academia | Chia-Tung Kuo University of California, Davis EMAIL S. S. Ravi University at Albany EMAIL Thi-Bich-Hanh Dao University of Orleans EMAIL Christel Vrain University of Orleans EMAIL Ian Davidson University of California, Davis EMAIL |
| Pseudocode | Yes | Figure 2: CP optimization encoding where the user provides a set of desired (feature-wise) diameters D as feedback. |
| Open Source Code | Yes | We provide enough details to reproduce our results and our code is made available2. 2https://sites.google.com/site/chiatungkuo/publication |
| Open Datasets | Yes | We apply our proposed approach to a network data set: Facebook-egonets from Stanford SNAP Data sets (Leskovec and Krevl 2014). |
| Dataset Splits | No | The paper mentions running k-means multiple times and selecting the best result, but does not provide specific train/validation/test split percentages, sample counts, or a detailed splitting methodology for reproducibility. |
| Hardware Specification | No | Consequently our experiments on the Facebook data (n = 4039, k = 4, f = 2) and f MRI data (n = 1730, k = 4, f = 2) each take less than 2 minutes to ο¬nish on a 12-core workstation. |
| Software Dependencies | No | Note that we chose to implement our model in the CP language Numberjack (Hebrard, O Mahony, and O Sullivan 2010) due to its simple interface and its use of state-of-the-art integer linear program (ILP) solvers. ILP solvers such as Gurobi (Inc. 2015) (used in our experiments) can easily exploit multi-core architectures. |
| Experiment Setup | Yes | We choose the upper and lower bounds according to the averages in the initial summary and set bounds [0.36, 0.4] for gender and [0.13, 0.15] for language so that these two features are balanced across clusters. |