Research map / Learning theory and algorithms / Clustering
Improved Guarantees for k-means++ and k-means++ Parallel
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++ (NeurIPS 2016 · similarity 0.90)
- Fast and Accurate $k$-means++ via Rejection Sampling (NeurIPS 2020 · similarity 0.89)
- k-means++: few more steps yield constant approximation (ICML 2020 · similarity 0.88)
- Modified K-means Algorithm with Local Optimality Guarantees (ICML 2025 · similarity 0.88)
- Relax and Merge: A Simple Yet Effective Framework for Solving Fair $k$-Means and $k$-sparse Wasserstein Barycenter Problems (ICLR 2025 · similarity 0.87)
- Approximately Pareto-optimal Solutions for Bi-Objective k-Clustering (NeurIPS 2024 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
