Agarwal, P.K., Har-Peled, S., Varadarajan, K.R.: Approximating extent measures of points. J. ACM 51(4), 606–635 (2004)
MathSciNet
CrossRef
Google Scholar
Agarwal, P.K., Har-Peled, S., Varadarajan, K.R.: Geometric approximation via coresets. In: Welzl, E., (ed.) Current Trends in Combinatorial and Computational Geometry (2007)
Google Scholar
Boutsidis, C., Mahoney, M.W., Drineas, P.: An improved approximation algorithm for the column subset selection problem. In: Proceedings of the Twentieth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2009, New York, NY, USA, 4–6 January 2009, pp. 968–977 (2009)
CrossRef
Google Scholar
Clarkson, K.L., Woodruff, D.P.: Low rank approximation and regression in input sparsity time. In: Symposium on Theory of Computing Conference, STOC 2013, Palo Alto, CA, USA, 1–4 June 2013, pp. 81–90 (2013)
Google Scholar
Cohen, M.B., Elder, S., Musco, C., Musco, C., Persu, M.: Dimensionality reduction for k-means clustering and low rank approximation. In: Proceedings of the Forty-Seventh Annual ACM on Symposium on Theory of Computing, STOC 2015, Portland, OR, USA, 14–17 June 2015, pp. 163–172 (2015)
Google Scholar
Ester, M., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD 1996), Portland, Oregon, USA, pp. 226–231 (1996)
Google Scholar
Feldman, D., Fiat, A., Sharir, M.: Coresets forweighted facilities and their applications. In: 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2006), 21–24 October 2006, Berkeley, California, USA, Proceedings, pp. 315–324 (2006)
Google Scholar
Feldman, D., Langberg, M.: A unified framework for approximating and clustering data. In: Proceedings of the 43rd ACM Symposium on Theory of Computing, STOC 2011, San Jose, CA, USA, 6–8 June 2011, pp. 569–578 (2011)
Google Scholar
Feldman, D., Monemizadeh, M., Sohler, C., Woodruff, D.P.: Coresets and sketches for high dimensional subspace approximation problems. In: Proceedings of the Twenty-First Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 630–649 (2010)
CrossRef
Google Scholar
Feldman, D., Schmidt, M., Sohler, C.: Turning big data into tiny data: Constant-size coresets for k-means, PCA and projective clustering. In: Proceedings of the Twenty-Fourth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1434–1453 (2013)
CrossRef
Google Scholar
Har-Peled, S.: No, coreset, no cry. In: Lodaya, K., Mahajan, M. (eds.) FSTTCS 2004: Foundations of Software Technology and Theoretical Computer Science. Lecture Notes in Computer Science, vol. 3328, pp. 324–335. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30538-5_27
CrossRef
Google Scholar
Har-Peled, S., Mazumdar, S.: On coresets for k-means and k-median clustering. In: Proceedings of the 36th Annual ACM Symposium on Theory of Computing, Chicago, IL, USA, 13–16 June 2004, pp. 291–300 (2004)
Google Scholar
Henzinger, M.R., Raghavan, P., Rajagopalan, S.: Computing on data streams. In: Proceedings of a DIMACS Workshop on External Memory Algorithms, New Brunswick, New Jersey, USA, 20–22 May 1998, pp. 107–118 (1998)
Google Scholar
Hinneburg, A., Aggarwal, C.C., Keim, D.A.: What is the nearest neighbor in high dimensional spaces? In: VLDB 2000, Proceedings of 26th International Conference on Very Large Data Bases, 10–14 September 2000, Cairo, Egypt, pp. 506–515 (2000)
Google Scholar
Mahoney, M.W.: Randomized algorithms for matrices and data. Found. Trends Mach. Learn. 3(2), 123–224 (2011)
MATH
Google Scholar
Phillips, J.M.: Coresets and sketches. CoRR, abs/1601.00617 (2016)
Google Scholar
Sarlós, T.: Improved approximation algorithms for large matrices via random projections. In: 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2006), 21–24 October 2006, Berkeley, California, USA, Proceedings, pp. 143–152 (2006)
Google Scholar
Varadarajan, K.R., Xiao, X.: A near-linear algorithm for projective clustering integer points. In: Proceedings of the Twenty-Third Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2012, Kyoto, Japan, 17–19 January 2012, pp. 1329–1342 (2012)
CrossRef
Google Scholar
Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: A new data clustering algorithm and its applications. Data Min. Knowl. Discov. 1(2), 141–182 (1997)
CrossRef
Google Scholar