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Yuewei Ming

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AAAI Conference 2018 Short Paper

Variance Reduced K-Means Clustering

  • Yawei Zhao
  • Yuewei Ming
  • Xinwang Liu
  • En Zhu
  • Jianping Yin

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.