Research map / Learning theory and algorithms / Clustering
The Catastrophic Failure of *the* k-Means Algorithm in High Dimensions, and How Hartigan's Algorithm Avoids It
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 Batched Hartigan's $k$-Means Clustering (NeurIPS 2026 · similarity 0.90)
- Simple, Scalable and Effective Clustering via One-Dimensional Projections (NeurIPS 2023 · similarity 0.88)
- The Complexity of k-Means Clustering when Little is Known (ICML 2022 · similarity 0.88)
- Power k-Means Clustering (ICML 2019 · similarity 0.87)
- Clustering Stable Instances of Euclidean k-means. (NeurIPS 2017 · similarity 0.87)
- Near-Linear Time Approximation Algorithms for k-means with Outliers (ICML 2024 · similarity 0.85)
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.