Provable Imbalanced Point Clustering

David Denisov; Dan Feldman; Shlomi Dolev; Michael Segal

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arXiv · 2408.14225

Provable Imbalanced Point Clustering

David Denisov·Dan Feldman·Shlomi Dolev·Michael Segal

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Abstract

We suggest efficient and provable methods to compute an approximation for imbalanced point clustering, that is, fitting $k$-centers to a set of points in $\mathbb{R}^d$, for any $d,k\geq 1$. To this end, we utilize \emph{coresets}, which, in the context of the paper, are essentially weighted sets of points in $\mathbb{R}^d$ that approximate the fitting loss for every model in a given set, up to a multiplicative factor of $1\pm\varepsilon$. We provide [Section 3 and Section E in the appendix] experiments that show the empirical contribution of our suggested methods for real images (novel and reference), synthetic data, and real-world data. We also propose choice clustering, which by combining clustering algorithms yields better performance than each one separately.

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David Denisov, Dan Feldman, Shlomi Dolev, Michael Segal. 2024-08-26. Provable Imbalanced Point Clustering. https://arxiv.org/abs/2408.14225

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