K-平均算法 (Chinese Wikipedia)

Analysis of information sources in references of the Wikipedia article "K-平均算法" in Chinese language version.

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13web.archive.org

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8doi.org

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3ams.org

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2zbmath.org

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2arxiv.org

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2uta.edu

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2ucsd.edu

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1projecteuclid.org

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1toronto.edu

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1jstor.org

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1cam.ac.uk

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1iitk.ac.in

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1stanford.edu

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1nrbook.com

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1maxlittle.net

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1gatech.edu

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1huji.ac.il

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ams.org

Steinhaus, H. Sur la division des corps matériels en parties. Bull. Acad. Polon. Sci. 1957, 4 (12): 801–804. MR 0090073. Zbl 0079.16403 （法语）.
MacQueen, J. B. Some Methods for classification and Analysis of Multivariate Observations. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability 1. University of California Press: 281–297. 1967 [2009-04-07]. MR 0214227. Zbl 0214.46201. （原始内容存档于2019-10-29）.
MacKay, David. Chapter 20. An Example Inference Task: Clustering (PDF). Information Theory, Inference and Learning Algorithms. Cambridge University Press. 2003: 284–292 [2015-04-02]. ISBN 0-521-64298-1. MR 2012999. （原始内容存档于2016-02-17）.

archive.org

E.W. Forgy. Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics. 1965, 21: 768–769.

arxiv.org

Dasgupta, S. and Freund, Y. Random Projection Trees for Vector Quantization. Information Theory, IEEE Transactions on. July 2009, 55: 3229–3242. arXiv:0805.1390 . doi:10.1109/TIT.2009.2021326.
Cohen, M.; S. Elder; C. Musco; C. Musco; M. Persu. Dimensionality reduction for k-means clustering and low rank approximation (Appendix B). ArXiv. 2014 [2014-11-29]. （原始内容存档于2020-08-06）.

cam.ac.uk

inference.phy.cam.ac.uk

MacKay, David. Chapter 20. An Example Inference Task: Clustering (PDF). Information Theory, Inference and Learning Algorithms. Cambridge University Press. 2003: 284–292 [2015-04-02]. ISBN 0-521-64298-1. MR 2012999. （原始内容存档于2016-02-17）.

doi.org

Lloyd, S. P. Least square quantization in PCM. Bell Telephone Laboratories Paper. 1957. Published in journal much later: Lloyd., S. P. Least squares quantization in PCM (PDF). IEEE Transactions on Information Theory. 1982, 28 (2): 129–137 [2009-04-15]. doi:10.1109/TIT.1982.1056489. （原始内容存档 (PDF)于2009-06-17）.
Vattani., A. k-means requires exponentially many iterations even in the plane (PDF). Discrete and Computational Geometry. 2011, 45 (4): 596–616 [2015-04-02]. doi:10.1007/s00454-011-9340-1. （原始内容存档 (PDF)于2011-10-08）.
Aloise, D.; Deshpande, A.; Hansen, P.; Popat, P. NP-hardness of Euclidean sum-of-squares clustering. Machine Learning. 2009, 75: 245–249. doi:10.1007/s10994-009-5103-0.
Dasgupta, S. and Freund, Y. Random Projection Trees for Vector Quantization. Information Theory, IEEE Transactions on. July 2009, 55: 3229–3242. arXiv:0805.1390 . doi:10.1109/TIT.2009.2021326.
Mahajan, M.; Nimbhorkar, P.; Varadarajan, K. The Planar k-Means Problem is NP-Hard. Lecture Notes in Computer Science. 2009, 5431: 274–285. doi:10.1007/978-3-642-00202-1_24.
Inaba, M.; Katoh, N.; Imai, H. Applications of weighted Voronoi diagrams and randomization to variance-based k-clustering. Proceedings of 10th ACM Symposium on Computational Geometry: 332–339. 1994. doi:10.1145/177424.178042.
Drineas, P.; A. Frieze; R. Kannan; S. Vempala; V. Vinay. Clustering large graphs via the singular value decomposition (PDF). Machine learning. 2004, 56: 9–33 [2012-08-02]. doi:10.1023/b:mach.0000033113.59016.96. （原始内容存档 (PDF)于2020-10-24）.

dx.doi.org

Honarkhah, M and Caers, J, 2010, Stochastic Simulation of Patterns Using Distance-Based Pattern Modeling, Mathematical Geosciences, 42: 487 - 517

gatech.edu

cc.gatech.edu

Drineas, P.; A. Frieze; R. Kannan; S. Vempala; V. Vinay. Clustering large graphs via the singular value decomposition (PDF). Machine learning. 2004, 56: 9–33 [2012-08-02]. doi:10.1023/b:mach.0000033113.59016.96. （原始内容存档 (PDF)于2020-10-24）.

huji.ac.il

cs.huji.ac.il

Alon Vinnikov and Shai Shalev-Shwartz. k-means Recovers ICA Filters when Independent Components are Sparse (PDF). Proc. of Int'l Conf. Machine Learning (ICML 2014). 2014 [2015-04-02]. （原始内容存档 (PDF)于2020-11-26）.

iitk.ac.in

cse.iitk.ac.in

Arthur; Abhishek Bhowmick. A theoretical analysis of Lloyd's algorithm for k-means clustering (学位论文). 2009. [1]^{[失效連結]}

jstor.org

Hartigan, J. A.; Wong, M. A. Algorithm AS 136: A k-Means Clustering Algorithm. Journal of the Royal Statistical Society, Series C. 1979, 28 (1): 100–108. JSTOR 2346830.

maxlittle.net

Little, M.A.; Jones, N.S. Generalized Methods and Solvers for Piecewise Constant Signals: Part I (PDF). Proceedings of the Royal Society A. 2011 [2015-04-02]. （原始内容存档 (PDF)于2019-08-19）.

nrbook.com

apps.nrbook.com

Press, WH; Teukolsky, SA; Vetterling, WT; Flannery, BP. Section 16.1. Gaussian Mixture Models and k-Means Clustering. Numerical Recipes: The Art of Scientific Computing 3rd. New York: Cambridge University Press. 2007 [2015-04-02]. ISBN 978-0-521-88068-8. （原始内容存档于2011-08-11）.

projecteuclid.org

MacQueen, J. B. Some Methods for classification and Analysis of Multivariate Observations. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability 1. University of California Press: 281–297. 1967 [2009-04-07]. MR 0214227. Zbl 0214.46201. （原始内容存档于2019-10-29）.

stanford.edu

Coates, Adam; Ng, Andrew Y. Learning feature representations with k-means (PDF). G. Montavon, G. B. Orr, K.-R. Müller (编). Neural Networks: Tricks of the Trade. Springer. 2012 [2015-04-02]. （原始内容 (PDF)存档于2013-07-06）.

toronto.edu

cs.toronto.edu

Lloyd, S. P. Least square quantization in PCM. Bell Telephone Laboratories Paper. 1957. Published in journal much later: Lloyd., S. P. Least squares quantization in PCM (PDF). IEEE Transactions on Information Theory. 1982, 28 (2): 129–137 [2009-04-15]. doi:10.1109/TIT.1982.1056489. （原始内容存档 (PDF)于2009-06-17）.

ucsd.edu

charlotte.ucsd.edu

Hamerly, G. and Elkan, C. Alternatives to the k-means algorithm that find better clusterings (PDF). Proceedings of the eleventh international conference on Information and knowledge management (CIKM). 2002 [2015-04-02]. （原始内容 (PDF)存档于2012-08-05）.

cseweb.ucsd.edu

Vattani., A. k-means requires exponentially many iterations even in the plane (PDF). Discrete and Computational Geometry. 2011, 45 (4): 596–616 [2015-04-02]. doi:10.1007/s00454-011-9340-1. （原始内容存档 (PDF)于2011-10-08）.

uta.edu

ranger.uta.edu

H. Zha, C. Ding, M. Gu, X. He and H.D. Simon. Spectral Relaxation for k-means Clustering (PDF). Neural Information Processing Systems vol.14 (NIPS 2001) (Vancouver, Canada). Dec 2001: 1057–1064 [2015-04-02]. （原始内容存档 (PDF)于2020-09-30）.
Chris Ding and Xiaofeng He. k-means Clustering via Principal Component Analysis (PDF). Proc. of Int'l Conf. Machine Learning (ICML 2004). July 2004: 225–232 [2015-04-02]. （原始内容存档 (PDF)于2020-09-30）.

web.archive.org

MacQueen, J. B. Some Methods for classification and Analysis of Multivariate Observations. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability 1. University of California Press: 281–297. 1967 [2009-04-07]. MR 0214227. Zbl 0214.46201. （原始内容存档于2019-10-29）.
Lloyd, S. P. Least square quantization in PCM. Bell Telephone Laboratories Paper. 1957. Published in journal much later: Lloyd., S. P. Least squares quantization in PCM (PDF). IEEE Transactions on Information Theory. 1982, 28 (2): 129–137 [2009-04-15]. doi:10.1109/TIT.1982.1056489. （原始内容存档 (PDF)于2009-06-17）.
MacKay, David. Chapter 20. An Example Inference Task: Clustering (PDF). Information Theory, Inference and Learning Algorithms. Cambridge University Press. 2003: 284–292 [2015-04-02]. ISBN 0-521-64298-1. MR 2012999. （原始内容存档于2016-02-17）.
Hamerly, G. and Elkan, C. Alternatives to the k-means algorithm that find better clusterings (PDF). Proceedings of the eleventh international conference on Information and knowledge management (CIKM). 2002 [2015-04-02]. （原始内容 (PDF)存档于2012-08-05）.
Vattani., A. k-means requires exponentially many iterations even in the plane (PDF). Discrete and Computational Geometry. 2011, 45 (4): 596–616 [2015-04-02]. doi:10.1007/s00454-011-9340-1. （原始内容存档 (PDF)于2011-10-08）.
Coates, Adam; Ng, Andrew Y. Learning feature representations with k-means (PDF). G. Montavon, G. B. Orr, K.-R. Müller (编). Neural Networks: Tricks of the Trade. Springer. 2012 [2015-04-02]. （原始内容 (PDF)存档于2013-07-06）.
Press, WH; Teukolsky, SA; Vetterling, WT; Flannery, BP. Section 16.1. Gaussian Mixture Models and k-Means Clustering. Numerical Recipes: The Art of Scientific Computing 3rd. New York: Cambridge University Press. 2007 [2015-04-02]. ISBN 978-0-521-88068-8. （原始内容存档于2011-08-11）.
Little, M.A.; Jones, N.S. Generalized Methods and Solvers for Piecewise Constant Signals: Part I (PDF). Proceedings of the Royal Society A. 2011 [2015-04-02]. （原始内容存档 (PDF)于2019-08-19）.
H. Zha, C. Ding, M. Gu, X. He and H.D. Simon. Spectral Relaxation for k-means Clustering (PDF). Neural Information Processing Systems vol.14 (NIPS 2001) (Vancouver, Canada). Dec 2001: 1057–1064 [2015-04-02]. （原始内容存档 (PDF)于2020-09-30）.
Chris Ding and Xiaofeng He. k-means Clustering via Principal Component Analysis (PDF). Proc. of Int'l Conf. Machine Learning (ICML 2004). July 2004: 225–232 [2015-04-02]. （原始内容存档 (PDF)于2020-09-30）.
Drineas, P.; A. Frieze; R. Kannan; S. Vempala; V. Vinay. Clustering large graphs via the singular value decomposition (PDF). Machine learning. 2004, 56: 9–33 [2012-08-02]. doi:10.1023/b:mach.0000033113.59016.96. （原始内容存档 (PDF)于2020-10-24）.
Cohen, M.; S. Elder; C. Musco; C. Musco; M. Persu. Dimensionality reduction for k-means clustering and low rank approximation (Appendix B). ArXiv. 2014 [2014-11-29]. （原始内容存档于2020-08-06）.
Alon Vinnikov and Shai Shalev-Shwartz. k-means Recovers ICA Filters when Independent Components are Sparse (PDF). Proc. of Int'l Conf. Machine Learning (ICML 2014). 2014 [2015-04-02]. （原始内容存档 (PDF)于2020-11-26）.

zbmath.org

Steinhaus, H. Sur la division des corps matériels en parties. Bull. Acad. Polon. Sci. 1957, 4 (12): 801–804. MR 0090073. Zbl 0079.16403 （法语）.
MacQueen, J. B. Some Methods for classification and Analysis of Multivariate Observations. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability 1. University of California Press: 281–297. 1967 [2009-04-07]. MR 0214227. Zbl 0214.46201. （原始内容存档于2019-10-29）.