[1] Steinhaus H. Sur la division des corp materiels en parties [J]. Bulletin L'Acad\acute{\rm e}mie Polonaise des Science, 1956, IV: 801-804.
[2] Lloyd, S. Least squares quantization in PCM [J]. IEEE Transactions on Information Theory, 1982, 28: 129-137.
[3] Ball G, Hall D. ISODATA, a novel method of data anlysis and pattern classification [R]. Technical report NTIS AD 699616, Stanford: Stanford Research Institute, 1965.
[4] MacQueen J. Some methods for classification and analysis of multivariate observations [C]// Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1967, 1(14): 281-297.
[5] Mahajan M, Nimbhorkar P, Varadarajan K. The planar k-means problem is NP-hard [J]. Theoretical Computer Science, 2009, 442(8): 13-21.
[6] Aloise D, Deshpande A, Hansen P, et al. NP-hardness of Euclidean sum-of-squares clustering [J]. Machine Learning, 2009, 75: 245-249.
[7] Awasthi P, Charikar M, Krishnaswamy R, et al. The hardness of approximation of Euclidean k-means [C]//Proceedings of the 31st International Symposium on Computational Geometry, 2015, 754-767.
[8] Lee E, Schmidt M, Wright J. Improved and simplified inapproximability for k-means [J]. Information Processing Letters, 2016, 120: 40-43.
[9] MacKay D J C. Information Theory, Inference and Learning Algorithms [M]. Cambridge: Cambridge University Press, 2003, 284-292.
[10] Arthur D, Vassilvitskii S. K-means++: the advantages of careful seeding [C]//Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms, 2007, 1027-1035.
[11] Ostrovsky R, Rabani Y, Schulman L J, et al. The effectiveness of Lloyd-type methods for the k-means problem [J]. Journal of the ACM, 2013, 59: 1-22.
[12] Arthur D, Manthey B, R\ddot{\rm o}glin H. Smoothed analysis of the k-means method [J]. Journal of the ACM, 2011, 58(5): 1-31.
[13] Kanungo T, Mount D M, Netanyahu N S, et al. A local search approximation algorithm for k-means clustering [C]//Proceedings of the 18th Annual Symposium on Computational Geometry, 2002, 10-18.
[14] Aggarwal A, Deshpande A, Kannan R. Adaptive sampling for k-means clustering [C]// Proceedings of the 12th International Workshop, APPROX, and 13th International Workshop, RANDOM, 2009, 15-28.
[15] Makarychev K, Makarychev Y, Sviridenko M, Ward J. A bi-criteria approximation algorithm for k-means [C]//Proceedings of APPROX/RONDOM'16, 2016, Article No. 14, 1-20.
[16] Hsu D, Telgarsky M. Greedy bi-criteria approximations for k-medians and k-means [J]. arXiv: 1607.06203, 2016.
[17] Bandyapadhyay S, Varadarajan K. On variants of k-means clustering [C]//Proceedings of the 32nd International Symposium on Computational Geometry, 2016, Article No. 14, 1-15.
[18] Inaba M, Katoh N, Imai H. Applications of weighted Voronoi diagrams and randomization to variance-based k-clustering [C]//Proceedings of the 10th Annual Symposium on Computational Geometry, 1994, 332-339.
[19] Matou\check{\rm s}ek J. On approximate geometric k-clustering [J]. Discrete and Computational Geometry, 2000, 24: 61-84.
[20] Friggstad Z, Rezapour M, Salavatipour M R. Local search yields a PTAS for k-means in doubling metrics [C]//Proceedings of the 57th Annual Symposium on Foundations of Computer Science, 2016, 365-374.
[21] Cohen-Addad V, Klein P N, Mathieu C. Local search yields approximation schemes for k-means and k-median in Euclidean and minor-free metrics [C]//Proceedings of the 57th Annual Symposium on Foundations of Computer Science, 2016, 353-364.
[22] Bradley P S, Mangasarian O L, Street W N. Clustering via concave minimization [M]//Advances in Neural Information Processing Systems, Cambridge, Massachusetts: MIT Press, 1997, 368-374.
[23] Jain A K, Dubes R C. Algorithms for Clustering Data [M]. London: Prentice-Hall, 1988.
[24] Kaufman L, Rousseeuw P. Clustering by Means of Medoids [M]. Amsterdam: North-Holland, 1987.
[25] Arya V, Garg N, Khandekar R, et al. Local search heuristic for k-median and facility location problems [C]//Proceedings of the 33rd Annual ACM Symposium on Theory of Computing, 2001, 21-29.
[26] Li S, Svensson O. Approximating k-median via pseudo-approximation [J]. SIAM Journal on Computing, 2016, 45: 530-547.
[27] Byrka J, Pensyl T, Rybicki B, et al. An improved approximation for k-median, and positive correlation in budgeted optimization [C]// Proceedings of the 26th Annual ACM-SIAM Symposium on Discrete Algorithms, 2015, 737-756.
[28] Byrka J, Pensyl T, Rybicki B, et al. An improved approximation for k-median, and positive correlation in budgeted optimization [J]. arXiv: 1406.2951v4, 2016.
[29] Huang Z. Extensions to the k-means algorithm for clustering large data sets with categorical values [J]. Data Mining and Knowledge Discovery, 1998, 2: 283-304.
[30] He Z, Deng S, Xu X. Approximation algorithms for k-modes clustering [C]//Proceedings of the International Conference on Intelligent Computing, 2006, 296-302.
[31] Aggarwal C C, Reddy C K. Data Clustering: Algorithms and Applications [M]. London: Chapman and Hall/CRC, 2013.
[32] Pelleg D, Moore A W. X-means: Extending k-means with efficient estimation of the number of clusters [C]//Proceedings of the International Conference on Machine Learning, 2000, 727-734.
[33] Hamerly G, Elkan C. Learning the k in k-means [C]//Proceedings of the Annual Conference on Neural Information Processing Systems, 2003, 281-288.
[34] Feng Y, Hamerly G, Elkan C. PG-means: learning the number of clusters in data [C]//Proceedings of the Annual Conference on Neural Information Processing Systems, 2006, 393-400.
[35] Ishioka T. An expansion of X-means for automatically determining the optimal number of clusters [C]//Proceedings of International Conference on Computational Intelligence, 2005, 91-95.
[36] Tucker C S, Kim H M, Barker D E, et al. A relief attribute weighting and X-means clustering methodology for top-down product family optimization [J]. Engineering Optimization, 2010, 42: 593-616.
[37] Dunn J C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters [J]. Journal of Cybernet, 1973, 3: 32-57.
[38] Celeux G, Govaert G. A classification EM algorithm for clustering and two stochastic versions [J]. Computational Statistics & Data Analysis, 1992, 14: 315-332.
[39] Ana L N F, Jain A K. Robust data clustering [C]//Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003, 2: 128-133.
[40] Honda K, Notsu A, Ichihashi H. Fuzzy PCA-Guided robust k-means clustering [J]. IEEE Transactions on Fuzzy Systems, 2010, 18: 67-79.
[41] Zhang D, Hao C, Wu C, et al. A local search approximation algorithm for the k-means problem with penalties [R]. Beijing: Beijing University of Technology, 2017.
[42] Gupta S, Kumar R, Lu K, et al. Local search methods for k-means with outliers [C]//Proceedings of the VLDB Endowment, 2017, 10(7): 757-768.
[43] Zhang D, Xu D, Zhang P, et al. A local search approximation algorithm for a sum of squares k-facility location problem [R]. Beijing: Beijing University of Technology, 2017.
[44] Zhang D, Xu D, Zhang P, et al. A local search approximation algorithm for the sum of squares facility location problem [R]. Beijing: Beijing University of Technology, 2017.
[45] Bradley P S, Bennett K P, Demiriz A. Constrained k-means clustering [J]. Microsoft Research, 2000, 59(1): 1-34.
[46] Luo M, Ma Y F, Zhang H J. A spatial constrained k-means approach to image segmentation [C]//Proceedings of the Joint Conference of the 4th International Conference on IEEE, 2003, 738-742.
[47] Ng M K. A note on constrained k-means algorithms [J]. Pattern Recognition, 2000, 33: 515-519.
[48] Wagstaff K, Cardie C, Rogers S, et al. Constrained k-means clustering with background knowledge [C]//Proceedings of the International Conference on Machine Learning, 2001, 577-584.
[49] Banerjee A, Merugu S, Dhillon I S, et al. Clustering with Bregman divergences [J]. Journal of Machine Learning Research, 2005, 6: 1705-1749.
[50] Kim K, Ahn H. A recommender system using GA k-means clustering in an online shopping market [J]. Expert Systems with Applications, 2008, 34: 1200-1209.
[51] King A. Online k-means clustering of nonstationary data [J]. Prediction Project Report, 2012, 1-9.
[52] Liberty E, Sriharsha R, Sviridenko M. An algorithm for online k-means clustering [C]// Proceedings of the 18th Workshop on Algorithm Engineering and Experiment Society for Industrial and Applied Mathematics, 2016, 81-89.
[53] Buchta C, Kober M, Feinerer I, et al. Spherical k-means clustering [J]. Journal of Statistical Software, 2012, 50: 1-22.
[54] Wild S. Seeding Non-Negative Matrix Factorizations with the Spherical k-Means Clustering [M]. Colorado: University of Colorado, 2003.
[55] Zhong S. Efficient online spherical k-means clustering [C]//Proceedings of the International Joint Conference on IEEE, 2005, 3180-3185.
[56] Hong Y, Kwong S. Learning assignment order of instances for the constrained k-means clustering algorithm [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2009, 39: 568-574.
[57] Ding C, Zhou D, He X, et al. R 1-PCA: rotational invariant L_1-norm principal component analysis for robust subspace factorization [C]// Proceedings of the 23rd International Conference on Machine Learning, 2006, 281-288.
[58] Wu X, Zhu X, Wu G Q, et al. Data mining with big data [J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26: 97-107.
[59] Cai X, Nie F, Huang H. Multi-view k-means clustering on big data [C]//Proceedings of the International Joint Conference on Artificial Intelligence, 2013, 2598-2604.
[60] Cui X, Zhu P, Yang X, et al. Optimized big data k-means clustering using MapReduce [J]. The Journal of Supercomputing, 2014, 70: 1249-1259.
[61] Ding H, Liu Y, Huang L, et al. K-Means clustering with distributed dimensions [C]//Proceedings of the 33rd International Conference on Machine Learning, 2016, 1339-1348.
[62] Shim K. MapReduce algorithms for big data analysis [C]//Proceedings of the Very Large Database Endowment Endowment, 2012, 2016-2017.
[63] Holmes A. Hadoop in Practice [M]. New York: Manning Publications Co., 2012.
[64] Slavakis K, Giannakis G B, Mateos G. Modeling and optimization for big data analytics: (statistical) learning tools for our era of data deluge [J]. IEEE Signal Processing Magazine, 2014, 31: 18-31.
[65] Chen C L P, Zhang C Y. Data-intensive applications, challenges, techniques and technologies: a survey on big data [J]. Information Sciences, 2014, 275: 314-347.
[66] Fan W, Bifet A. Mining big data: current status, and forecast to the future [J]. ACM SIGKDD Explorations Newsletter, 2013, 14: 1-5.
[67] Dempster A P, Laird N M, Rubin D B. Maximum likelihood from incomplete data via the EM algorithm [J]. Journal of the Royal Statistical Society, Series B (Methodological), 1977, 39: 1-38.