[16] James Briggs. Faiss: The missing manual. https://www.pinecone.io/learn/faiss/.
[17] Jarosław Byrka, Thomas Pensyl, Bartosz Rybicki, Aravind Srinivasan, and Khoa Trinh. An improved approximation for k-median, and positive correlation in budgeted optimization. In Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA '15, page 737-756, USA, 2015. Society for Industrial and Applied Mathematics.
[18] Marco Capó, Aritz Pérez, and Jose A Lozano. An efficient k-means clustering algorithm for massive data. arXiv preprint arXiv:1801.02949, 2018.
[19] Ke Chen. On coresets for k-median and k-means clustering in metric and Euclidean spaces and their applications. SIAM Journal on Computing, 39(3):923-947, 2009.
[20] Davin Choo, Christoph Grunau, Julian Portmann, and Vaclav Rozhon. k-means++: few more steps yield constant approximation. In Hal Daumé III and Aarti Singh, editors, Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 1909–1917. PMLR, 13–18 Jul 2020.
[21] Michael B. Cohen, Sam Elder, Cameron Musco, Christopher Musco, and Madalina Persu. Dimensionality reduction for k-means clustering and low rank approximation. In Proceedings of the Forty-Seventh Annual ACM Symposium on Theory of Computing, STOC '15, page 163-172, New York, NY, USA, 2015. Association for Computing Machinery.
[22] Vincent Cohen-Addad. A fast approximation scheme for low-dimensional k-means. In Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA '18, page 430-440, USA, 2018. Society for Industrial and Applied Mathematics.
[23] Vincent Cohen-Addad, Andreas Emil Feldmann, and David Saulpic. Near-linear time approximation schemes for clustering in doubling metrics. J. ACM, 68(6), oct 2021.
[24] Vincent Cohen-Addad, Philip N. Klein, and Claire Mathieu. Local search yields approximation schemes for k-means and k-median in Euclidean and minor-free metrics. In 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS), pages 353–364, 2016.
[25] Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, and Chris Schwiegelshohn. Towards optimal lower bounds for k-median and k-means coresets. In Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing, pages 1038–1051, 2022.
[26] Vincent Cohen-Addad, Silvio Lattanzi, Ashkan Norouzi-Fard, Christian Sohler, and Ola Svensson. Fast and accurate k-means++ via rejection sampling. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 16235–16245. Curran Associates, Inc., 2020.
[27] Vincent Cohen-Addad, David Saulpic, and Chris Schwiegelshohn. A new coreset framework for clustering. In Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing, pages 169–182, 2021.
[28] Ryan R Curtin. A dual-tree algorithm for fast k-means clustering with large k. In Proceedings of the 2017 SIAM International Conference on Data Mining, pages 300-308. SIAM, 2017.
[29] Yufei Ding, Yue Zhao, Xipeng Shen, Madanlal Musuvathi, and Todd Mytkowicz. Yinyang k-means: A drop-in replacement of the classic k-means with consistent speedup. In Francis Bach and David Blei, editors, Proceedings of the 32nd International Conference on Machine Learning, volume 37 of Proceedings of Machine Learning Research, pages 579-587, Lille, France, 07-09 Jul 2015. PMLR.
[30] Jonathan Drake. Faster  $k$ -means clustering. PhD thesis, Baylor University, 2013.
[31] Petros Drineas, Alan Frieze, Ravi Kannan, Santosh Vempala, and Vishwanathan Vinay. Clustering large graphs via the singular value decomposition. Machine learning, 56:9-33, 2004.
[32] Dheeru Dua and Casey Graff. UCI machine learning repository, 2017.