arxiv
PublishedMay 11, 2026 at 4:00 AM
▲bullish
Simple KNN-Based Outlier Detection Achieves Robust Clustering
Publisher summary· verbatim
arXiv:2605.07130v1 Announce Type: new Abstract: Being robust to the presence of outliers is crucial for applying clustering algorithms in practice. In the $\textit{robust $k$-Means}$ problem (i.e., $k$-Means with outliers), the goal is to remove $z$ outliers and minimize the $k$-Means cost on the re
Stay posted· Newsletter
A 5-min weekly brief — top movers, price watch, story of the week.
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivReverso: Efficient Time Series Foundation Models for Zero-shot Forecasting5harxivMultinex: Lightweight Low-light Image Enhancement via Multi-prior Retinex5harxivMarket Design for AI: Beyond the Copyright Binary5harxivWho Pays the Price? Stakeholder-Centric Prompt Injection Benchmarking for Real-world Web Agents5hThe Bubble Brief
WEEKLYRead clustering insights every Tuesday — top movers, new releases, story of the week.
Originally published on arxiv ↗