Almost-linear Time Approximation Algorithm to Euclidean $k$-median and $k$-means
<|reference_start|>Almost-linear Time Approximation Algorithm to Euclidean $k$-median and $k$-means: Clustering is one of the staples of data analysis and unsupervised learning. As such, clustering algorithms are often used on massive data sets, and they need to be extremely fast. We focus on the Euclidean $k$-median and $k$-means problems, two of the standard ways to model the task of clustering.   For these, the go-to algorithm is $k$-means++, which yields an $O(\log k)$-approximation in time $\tilde O(nkd)$. While it is possible to improve either the approximation factor [Lattanzi and Sohler, ICML19] or the running time [Cohen-Addad et al., NeurIPS 20], it is unknown how precise a linear-time algorithm can be.   In this paper, we almost answer this question by presenting an almost linear-time algorithm to compute a constant-factor approximation.<|reference_end|>
@article{la tour2024almost-linear,
    title={Almost-linear Time Approximation Algorithm to Euclidean $k$-median and
  $k$-means},
    author={Max Dupr'e la Tour, David Saulpic},
    journal={arXiv preprint arXiv:2407.11217},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11217},
    primaryClass={cs.DS cs.AI}
}