@inproceedings{sculley2010web,
  title={Web-scale k-means clustering},
  author={Sculley, David},
  booktitle={Proceedings of the 19th international conference on World wide web},
  pages={1177--1178},
  year={2010},
  organization={ACM}
}

@inproceedings{arthur2007k,
  title={k-means++: The advantages of careful seeding},
  author={Arthur, David and Vassilvitskii, Sergei},
  booktitle={Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms},
  pages={1027--1035},
  year={2007},
  organization={Society for Industrial and Applied Mathematics}
}

@article{Hicks2020,
	author = {Hicks, Stephanie C. and Liu, Ruoxi and Ni, Yuwei and Purdom, Elizabeth and Risso, Davide},
	title = {mbkmeans: fast clustering for single cell data using mini-batch k-means},
	elocation-id = {2020.05.27.119438},
	year = {2020},
	doi = {10.1101/2020.05.27.119438},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {Single-cell RNA-Sequencing (scRNA-seq) is the most widely used high-throughput technology to measure genome-wide gene expression at the single-cell level. One of the most common analyses of scRNA-seq data detects distinct subpopulations of cells through the use of unsupervised clustering algorithms. However, recent advances in scRNA-seq technologies result in current datasets ranging from thousands to millions of cells. Popular clustering algorithms, such as k-means, typically require the data to be loaded entirely into memory and therefore can be slow or impossible to run with large datasets. To address this problem, we developed the mbkmeans R/Bioconductor package, an open-source implementation of the mini-batch k-means algorithm. Our package allows for on-disk data representations, such as the common HDF5 file format widely used for single-cell data, that do not require all the data to be loaded into memory at one time. We demonstrate the performance of the mbkmeans package using large datasets, including one with 1.3 million cells. We also highlight and compare the computing performance of mbkmeans against the standard implementation of k - means. Our software package is available in Bioconductor at https://bioconductor.org/packages/mbkmeans.Competing Interest StatementThe authors have declared no competing interest.},
	URL = {https://www.biorxiv.org/content/early/2020/05/27/2020.05.27.119438},
	eprint = {https://www.biorxiv.org/content/early/2020/05/27/2020.05.27.119438.full.pdf},
	journal = {bioRxiv}
}
