Fully Dynamic $k$-Clustering in $\tilde{O}(k)$ Update Time

Abstract

We present a $O(1)$-approximate fully dynamic algorithm for the $k$-median and $k$-means problems on metric spaces with amortized update time $\tilde{O}(k)$ and worst-case query time $\tilde{O}(k^2)$. We complement our theoretical analysis with the first in-depth experimental study for the dynamic $k$-median problem on general metrics, focusing on comparing our dynamic algorithm to the current state-of-the-art by Henzinger and Kale [ESA'20]. Finally, we also provide a lower bound for dynamic $k$-median which shows that any $O(1)$-approximate algorithm with $\tilde{O}(\text{poly}(k))$ query time must have $\tilde{\Omega }(k)$ amortized update time, even in the incremental setting.

Accepted at NeurIPS 2023