arXiv Machine Learning July 3, 2026 By Xin Guan

Statistical Properties of $k$-means Clustering for Data Missing Completely at Random

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arXiv:2607. 01945v1 Announce Type: cross Abstract: The classical $k$-means clustering cannot be directly used to incomplete data, and existing $k$-means-based clustering for missing data primarily focus on improving the practical accuracy of clustering, whereas most of them lack theoretical guarantees in the asymptotic sense.

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arXiv Machine Learning

Jul 1

MNAR-$k$-means: A $k$-means Clustering for Data Missing Not at Random with Magnitude-Decaying Probability

arXiv:2606. 31253v1 Announce Type: cross Abstract: The classical $k$-means clustering, based on distances computed from all data features, cannot be directly applied to incomplete data with missing values.

By Xin Guan

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arXiv Machine Learning

Sep 10

Beyond Worst-Case Coreset Bounds for $k$-Clustering via Determinantal Sampling

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4d ago

Federated Clustering with Unknown Local and Global Cluster Cardinalities

arXiv:2609.36762v1 Announce Type: new Abstract: Federated clustering methods that do not require the global number of clusters $K$ still assume that each client knows its local number $K_g$. This ass...

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arXiv Machine Learning

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Randomizing the Number of Centers in k-means++

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By Vaclav Rozhon

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High-Dimensional Gaussian Mean Estimation under Realizable Contamination

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By Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas

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