No More Than 6ft Apart: Robust K-Means via Radius Upper Bounds. (arXiv:2203.02502v1 [cs.LG])

Centroid based clustering methods such as k-means, k-medoids and k-centers
are heavily applied as a go-to tool in exploratory data analysis. In many
cases, those methods are used to obtain representative centroids of the data
manifold for visualization or summarization of a dataset. Real world datasets
often contain inherent abnormalities, e.g., repeated samples and sampling bias,
that manifest imbalanced clustering. We propose to remedy such a scenario by
introducing a maximal radius constraint $r$ on the clusters formed by the …

arxiv k-means