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 Better k-means++ Algorithm via Local Search
Authors: Silvio Lattanzi, Christian Sohler
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our algorithm empirically and show that it also improves the quality of the solution in practice. |
| Researcher Affiliation | Industry | 1Google Research, Zurich, ZH, Switzerland. Correspondence to: Silvio Lattanzi <EMAIL>, Christian Sohler <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 k-means++ seeding with local search Algorithm 2 Local Search++ |
| Open Source Code | No | The paper does not provide any explicit statements about making the source code available or include links to code repositories. |
| Open Datasets | Yes | RNA 8 features from 488565 RNA input sequence pairs (Uzilov et al., 2006) KDD-BIO 145751 samples with 74 features measuring the match between a protein and a native sequence (KDD) KDD-PHY 100000 samples with 78 features representing a quantum physic task (KDD) |
| Dataset Splits | No | The paper discusses applying k-means clustering to datasets but does not specify any training, validation, or test dataset splits. K-means is often applied to the entire dataset. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | We consider the k-means clustering for k = 25 or 50 on 3 different datasets: We stop the Lloyd s algorithm when the incremental improvement of an iteration was small. In particular, after 10 steps of Lloyd the improvement that we observed was less 0.4% per iteration for all considered datasets and for all different choices on the number of centers. |