Anupam Gupta and Kanat Tangwongsan. Simpler analyses of local search algorithms for facility location. CoRR, abs/0809.2554, 2008. URL http://arxiv.org/abs/0809.2554.
Venkatesan Guruswami and Piotr Indyk. Embeddings and non-approximability of geometric problems. In Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms, January 12-14, 2003, Baltimore, Maryland, USA., pages 537-538, 2003. URL http://dl.acm.org/citation.cfm?id=644108.644198.
Mary Inaba, Naoki Katoh, and Hiroshi Imai. Applications of weighted voronoi diagrams and randomization to variance-based k-clustering. In Proceedings of the tenth annual symposium on Computational geometry, pages 332-339, 1994.
Ragesh Jaiswal, Amit Kumar, and Sandeep Sen. A simple D 2-sampling based PTAS for k-means and other clustering problems. Algorithmica, 70(1):22-46, 2014. doi: 10.1007/s00453-013-9833-9. URL https://doi.org/10.1007/s00453-013-9833-9.
Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine D. Piatko, Ruth Silverman, and Angela Y. Wu. A local search approximation algorithm for k-means clustering. Computational Geometry, 28(2):89-112, 2004. ISSN 0925-7721. doi: https://doi.org/10.1016/j.comgeo.2004.03.003. URL https://www.sciencedirect.com/science/article/pii/S0925772104000215. Special Issue on the 18th Annual Symposium on Computational Geometry - SoCG2002.
Madhukar R. Korupolu, C. Greg Plaxton, and Rajmohan Rajaraman. Analysis of a local search heuristic for facility location problems. J. Algorithms, 37(1):146-188, 2000. doi: 10.1006/jagm.2000.1100. URL http://dx.doi.org/10.1006/jagm.2000.1100.
Amit Kumar, Yogish Sabharwal, and Sandeep Sen. Linear-time approximation schemes for clustering problems in any dimensions. J. ACM, 57(2):5:1-5:32, 2010. doi: 10.1145/1667053.1667054. URL https://doi.org/10.1145/1667053.1667054.
Silvio Lattanzi and Christian Sohler. A better k-means++ algorithm via local search. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 3662-3671. PMLR, 2019. URL https://proceedings.mlr.press/v97/lattanzi19a.html.
Euiwoong Lee, Melanie Schmidt, and John Wright. Improved and simplified inapproximability for k-means. Inf. Process. Lett., 120:40-43, 2017. doi: 10.1016/j.ipl.2016.11.009. URL https://doi.org/10.1016/j.ipl.2016.11.009.
SP Lloyd. Least square quantization in pmc. bell telephone laboratories paper. published in journal much later: Lloyd, sp: Least squares quantization in pmc. IEEE Trans. Inform. Theor.(1957/1982), 18, 1957.
Konstantin Makarychev, Yury Makarychev, Maxim Sviridenko, and Justin Ward. A bi-criteria approximation algorithm for k-means. In Klaus Jansen, Claire Mathieu, Jose D. P. Rolim, and Chris Umans, editors, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, APPROX/RANDOM 2016, September 7-9, 2016, Paris, France, volume 60 of LIPIcs, pages 14:1-14:20. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2016. doi: 10.4230/LIPiCs. APPROX-RANDOM.2016.14. URL https://doi.org/10.4230/LIPiCs.APPROX-RANDOM.2016.14.
Konstantin Makarychev, Yury Makarychev, and Ilya P. Razenshteyn. Performance of johnson-lindenstrauss transform for  $k$ -means and  $k$ -medians clustering. In Moses Charikar and Edith Cohen, editors, Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, STOC 2019, Phoenix, AZ, USA, June 23-26, 2019, pages 1027-1038. ACM, 2019. doi: 10.1145/3313276.3316350. URL https://doi.org/10.1145/3313276.3316350.
Jiri Matousek. On approximate geometric k-clustering. Discrete & Computational Geometry, 24(1):61-84, 2000. doi: 10.1007/s004540010019. URL http://dx.doi.org/10.1007/s004540010019.
Andrew V Uzilov, Joshua M Keegan, and David H Mathews. Detection of non-coding rnas on the basis of predicted secondary structure formation free energy change. BMC bioinformatics, 7(1): 1-30, 2006.