Simple and sharp analysis of k-means||

Václav Rozhoň
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8266-8275, 2020.

Abstract

We present a simple analysis of k-means|| (Bahmani et al., PVLDB 2012) - a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007). Moreover, the bound on the number of rounds is improved from $O(\log n)$ to $O(\log n / \log\log n)$, which we show to be tight.

Cite this Paper

BibTeX
@InProceedings{pmlr-v119-rozhon20a, title = {Simple and sharp analysis of k-means||}, author = {Rozho{\v{n}}, V{\'a}clav}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {8266--8275}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/rozhon20a/rozhon20a.pdf}, url = {https://proceedings.mlr.press/v119/rozhon20a.html}, abstract = {We present a simple analysis of k-means|| (Bahmani et al., PVLDB 2012) - a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007). Moreover, the bound on the number of rounds is improved from $O(\log n)$ to $O(\log n / \log\log n)$, which we show to be tight.} }
Endnote
%0 Conference Paper %T Simple and sharp analysis of k-means|| %A Václav Rozhoň %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-rozhon20a %I PMLR %P 8266--8275 %U https://proceedings.mlr.press/v119/rozhon20a.html %V 119 %X We present a simple analysis of k-means|| (Bahmani et al., PVLDB 2012) - a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007). Moreover, the bound on the number of rounds is improved from $O(\log n)$ to $O(\log n / \log\log n)$, which we show to be tight.
APA
Rozhoň, V.. (2020). Simple and sharp analysis of k-means||. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:8266-8275 Available from https://proceedings.mlr.press/v119/rozhon20a.html.

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