Variance Reduced K-Means Clustering

Authors

  • Yawei Zhao National University of Defense Technology
  • Yuewei Ming National University of Defense Technology
  • Xinwang Liu National University of Defense Technology
  • En Zhu National University of Defense Technology
  • Jianping Yin Dongguan University of Technology

DOI:

https://doi.org/10.1609/aaai.v32i1.12135

Keywords:

clustering, k-means clustering, variance reduction

Abstract

It is challenging to perform k-means clustering on a large scale dataset efficiently. One of the reasons is that k-means needs to scan a batch of training data to update the cluster centers at every iteration, which is time-consuming. In the paper, we propose a variance reduced k-mean VRKM, which outperforms the state-of-the-art method, and obtain 4× speedup for large-scale clustering. The source code is available on https://github.com/YaweiZhao/VRKM_sofia-ml.

AAAI 2018 Proceedings Cover

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Published

2018-04-29

How to Cite

Zhao, Y., Ming, Y., Liu, X., Zhu, E., & Yin, J. (2018). Variance Reduced K-Means Clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.12135

Issue

Vol. 32 No. 1 (2018): Thirty-Second AAAI Conference on Artificial Intelligence

Section

Student Abstract Track