Scalable k -Means Clustering via Lightweight Coresets-Reference-Cited by-同舟云学术

Scalable k -Means Clustering via Lightweight Coresets

Published:2018-07-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
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Author:

Bachem Olivier¹,Lucic Mario²,Krause Andreas³

Affiliation:

1. Google Brain &ETH Zurich, Zurich, Switzerland

2. Google Brain, Zurich, Switzerland

3. ETH Zurich, Zurich, Switzerland

Funder

European Research Council

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Google

International Business Machines Corporation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3219819.3219973

Reference26 articles.

1. Olivier Bachem Mario Lucic and Andreas Krause . 2015. Coresets for Nonparametric Estimation - the Case of DP-Means International Conference on Machine Learning. Olivier Bachem Mario Lucic and Andreas Krause . 2015. Coresets for Nonparametric Estimation - the Case of DP-Means International Conference on Machine Learning.

2. Olivier Bachem Mario Lucic and Silvio Lattanzi . 2018. One-Shot Coresets: The Case of k-Clustering. In Artificial Intelligence and Statistics. Olivier Bachem Mario Lucic and Silvio Lattanzi . 2018. One-Shot Coresets: The Case of k-Clustering. In Artificial Intelligence and Statistics.

3. Maria-Florina Balcan Steven Ehrlich and Yingyu Liang . 2013. Distributed k -Means and k -Median Clustering on General Topologies Neural Information Processing Systems. Maria-Florina Balcan Steven Ehrlich and Yingyu Liang . 2013. Distributed k -Means and k -Median Clustering on General Topologies Neural Information Processing Systems.

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