Competitively Consistent Clustering
Authors: Niv Buchbinder, Roie Levin, Yue Yang
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we evaluate our three clustering algorithms experimentally on UCI Machine Learning repository datasets. Our experiments demonstrate not only that our algorithms are simple to implement in practice, but that they also significantly outperforms the worst-case bound predicted by the theorem. |
| Researcher Affiliation | Academia | 1Department of Statistics and Operations Research, School of Mathematical Sciences, Tel Aviv University, Israel. 2Department of Computer Science, Rutgers University, Piscataway, NJ 08854. |
| Pseudocode | No | The paper describes algorithms using structured bullet points within the text (e.g., in Section 3.1, 'The Algorithm: Our algorithm maintains at time t a set of open centers St. ... Drop from St any i such that ... Iteratively: as long as there exists a client j Ct such that ...'), but does not feature a dedicated 'Pseudocode' or 'Algorithm' block with a distinct figure or styling. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code, nor does it provide any links to a code repository. |
| Open Datasets | Yes | We evaluate our three clustering algorithms experimentally on UCI Machine Learning repository datasets: Glass Identification (German, 1987), Wine Quality (Cortez et al., 2009), and Airfoil Self-Noise (Brooks et al., 2014). |
| Dataset Splits | No | The paper describes a dynamic data streaming setup for experiments: 'At each time t, either a new data point was inserted with probability 9/10, or an existing data point was deleted with probability 1/10.' However, it does not provide explicit training/test/validation dataset splits, as it operates in a fully dynamic online model. |
| Hardware Specification | No | The paper describes the experimental methodology and datasets used in Section 4 and Appendix D, but it does not provide any specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory, or processing units). |
| Software Dependencies | No | The paper describes the implementation and evaluation of algorithms, but it does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | We set k = 4 (for k-center and k-median), β = 3/2 and ϵ = 1/4. |