TY - JOUR AU - Zhao, Yawei AU - Ming, Yuewei AU - Liu, Xinwang AU - Zhu, En AU - Yin, Jianping PY - 2018/04/29 Y2 - 2024/03/29 TI - Variance Reduced K-Means Clustering JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 32 IS - 1 SE - Student Abstract Track DO - 10.1609/aaai.v32i1.12135 UR - https://ojs.aaai.org/index.php/AAAI/article/view/12135 SP - AB - <p> 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. </p> ER -