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..
XClusters: Explainability-First Clustering
Authors: Hyunseung Hwang, Steven Euijong Whang
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that our method can improve the explainability of any clustering that fits in our framework. |
| Researcher Affiliation | Academia | Hyunseung Hwang, Steven Euijong Whang KAIST EMAIL |
| Pseudocode | Yes | Algorithm 1: XClusters algorithm |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of the methodology's code. |
| Open Datasets | Yes | DS4C (Kim 2020): a public COVID-19 dataset containing patient data, policy data, and provincial data released by the Korea Centers for Disease Control & Prevention (KCDC). We use floating population data of the city of Seoul for each age and gender group (Jan. 2020 May 2020). Contracts (Linville 2022): a public contract dataset maintained by the State of Washington. |
| Dataset Splits | No | The paper mentions searching k within a range and repeating experiments, but it does not provide specific training, validation, and test dataset splits for reproducibility. |
| Hardware Specification | Yes | All experiments are performed on a server with Intel Xeon Gold 5115 CPUs. |
| Software Dependencies | No | The paper mentions using “Scikit-learn (Pedregosa et al. 2011) for the decision tree training” but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | We search k within the range [3, 4, . . . , 11] for all datasets. For XClusters, we set λ = 1, and ϵb = 0.05 as default values. We repeat each experiment 10 times. |