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 [1].
Selective inference for k-means clustering
Authors: Yiqun T. Chen, Daniela M. Witten
JMLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our proposal on hand-written digits data and on single-cell RNA-sequencing data. [...] 5. Simulation study [...] 6. Real data applications |
| Researcher Affiliation | Academia | Yiqun T. Chen EMAIL Data Science Institute and Department of Biomedical Data Science Stanford University Stanford, CA 94305, USA Daniela M. Witten EMAIL Departments of Statistics and Biostatistics University of Washington Seattle, WA 98195-4322, USA |
| Pseudocode | Yes | Algorithm 1: Lloyd s algorithm for k-means clustering (Lloyd, 1982) |
| Open Source Code | Yes | Methods developed in this paper are implemented in the R package Kmeans Inference, available at https://github.com/yiqunchen/Kmeans Inference. Data and code for reproducing the results in this paper can be found at https://github.com/yiqunchen/Kmeans Inference-experiments. |
| Open Datasets | Yes | 6.1 MNIST Dataset (Lecun et al., 1998) [...] 6.2 Single-cell RNA-sequencing data (Zheng et al., 2017) |
| Dataset Splits | No | The paper discusses dataset splitting in the context of demonstrating why naive p-values fail ("we divide the observations into a training and a test set") but does not provide explicit train/test/validation splits for its own experimental evaluations in Sections 5 and 6, which focus on hypothesis testing rather than predictive modeling. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper mentions "Methods developed in this paper are implemented in the R package Kmeans Inference" and "We fit a regression spline using the gam function in the R package mgcv (Wood, 2017)". However, it does not provide specific version numbers for these software components or the R environment itself. |
| Experiment Setup | Yes | We simulate 3,000 datasets with n = 150, σ = 1, and q = 2, 10, 50, 100. For each simulated dataset, we apply k-means clustering with K = 3...We consider four p-values... and compare... at level α = 0.05. [...] We apply k-means clustering with K = 6. [...] We applied k-means clustering to the transformed data with K = 5 |