arXiv Machine Learning July 3, 2026 By Xin Guan

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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

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