NeurIPS 2018poster31 citations
A Practical Algorithm for Distributed Clustering and Outlier Detection
Jiecao Chen, Erfan Sadeqi Azer, Qin Zhang
Abstract
We study the classic k-means/median clustering, which are fundamental problems in unsupervised learning, in the setting where data are partitioned across multiple sites, and where we are allowed to discard a small portion of the data by labeling them as outliers. We propose a simple approach based on constructing small summary for the original dataset. The proposed method is time and communication efficient, has good approximation guarantees, and can identify the global outliers effectively.
BibTeX
@inproceedings{NEURIPS2018_f7f580e1,
author = {Chen, Jiecao and Sadeqi Azer, Erfan and Zhang, Qin},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {A Practical Algorithm for Distributed Clustering and Outlier Detection},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/f7f580e11d00a75814d2ded41fe8e8fe-Paper.pdf},
volume = {31},
year = {2018}
}