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Clustering is a fundamental problem in statistics and machine learning.
Estimating the components of a mixture of normal distributions
Neil E Day · 1969
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Maximum likelihood from incomplete data via the em algorithm
Arthur P Dempster, Nan M Laird, and Donald B Rubin · 1977
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Maximum likelihood estimation of observer error-rates using the em algorithm
Alexander Philip Dawid and Allan M Skene · 1979
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An examination of the effect of six types of error perturbation on fifteen clustering algorithms
Glenn W Milligan · 1980
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Strong consistency of k k -means clustering
David Pollard et al · 1981
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Least squares quantization in pcm
Stuart Lloyd · 1982
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A central limit theorem for k-means clustering
David Pollard · 1982
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Stochastic blockmodels: First steps
Paul W Holland, Kathryn Blackmond Laskey, and Samuel Leinhardt · 1983
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Statistical analysis of finite mixture distributions
D Michael Titterington, Adrian FM Smith, and Udi E Makov · 1985
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Empirical processes: theory and applications
David Pollard · 1990
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Multivariate normal mixtures: a fast consistent method of moments
Bruce G Lindsay and Prasanta Basak · 1993
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Full reconstruction of markov models on evolutionary trees: identifiability and consistency
Joseph T Chang · 1996
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On convergence properties of the em algorithm for gaussian mixtures
Lei Xu and Michael I Jordan · 1996
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Large-scale clustering of cdna-fingerprinting data
Ralf Herwig, Albert J Poustka, Christine Müller, Christof Bull, Hans Lehrach, and John O’Brien · 1999
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Spectral partitioning of random graphs
Frank McSherry · 2001
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Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma
Gavin J Gordon, Roderick V Jensen, Li-Li Hsiao, Steven R Gullans, Joshua E Blumenstock, Sridhar Ramaswamy, William G Richards, David J Sugarbaker, and Raphael Bueno · 2002
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k-means projective clustering
Pankaj K Agarwal and Nabil H Mustafa · 2004
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Bagboosting for tumor classification with gene expression data
Marcel Dettling · 2004
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A spectral algorithm for learning mixture models
Santosh Vempala and Grant Wang · 2004
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On spectral learning of mixtures of distributions
Dimitris Achlioptas and Frank McSherry · 2005
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The political blogosphere and the 2004 us election: divided they blog
Lada A Adamic and Natalie Glance · 2005
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The spectral method for general mixture models
Ravindran Kannan, Hadi Salmasian, and Santosh Vempala · 2005
Cited alongside, same era.
How slow is the k-means method?
David Arthur and Sergei Vassilvitskii · 2006
Cited alongside, same era.
The effectiveness of lloyd-type methods for the k-means problem
Rafail Ostrovsky, Yuval Rabani, Leonard J Schulman, and Chaitanya Swamy · 2006
Cited alongside, same era.
k-means++: The advantages of careful seeding
David Arthur and Sergei Vassilvitskii · 2007
Cited alongside, same era.
A probabilistic analysis of em for mixtures of separated, spherical gaussians
Sanjoy Dasgupta and Leonard Schulman · 2007
Cited alongside, same era.
Learning mixtures of spherical gaussians: moment methods and spectral decompositions
Daniel Hsu and Sham M Kakade · 2013
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Consistency of spectral clustering in sparse stochastic block models
Jing Lei and Alessandro Rinaldo · 2013
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Statistical guarantees for the em algorithm: From population to sample-based analysis
Sivaraman Balakrishnan, Martin J Wainwright, and Bin Yu · 2014
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Important feature pca for high dimensional clustering
Jiashun Jin and Wanjie Wang · 2014
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A fast version of the k-means classification algorithm for astronomical applications
I Ordovás-Pascual and J Sánchez Almeida · 2014
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The hardness of k-means clustering
Sanjoy Dasgupta · 2008
Cited alongside, same era.
Top 10 algorithms in data mining
Xindong Wu, Vipin Kumar, J Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J McLachlan, Angus Ng, Bing Liu, S Yu Philip, et al · 2008
Cited alongside, same era.
Adaptive sampling for k-means clustering
Ankit Aggarwal, Amit Deshpande, and Ravi Kannan · 2009
Cited alongside, same era.
Spectral algorithms
Ravindran Kannan and Santosh Vempala · 2009
Cited alongside, same era.
The planar k-means problem is np-hard
Meena Mahajan, Prajakta Nimbhorkar, and Kasturi Varadarajan · 2009
Cited alongside, same era.
Clustering with spectral norm and the k-means algorithm
Amit Kumar and Ravindran Kannan · 2010
Cited alongside, same era.
Nonconvex statistical optimization: Minimax-optimal sparse pca in polynomial time
Zhaoran Wang, Huanran Lu, and Han Liu · 2014
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Alternating minimization for mixed linear regression
Xinyang Yi, Constantine Caramanis, and Sujay Sanghavi · 2014
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Spectral methods meet em: A provably optimal algorithm for crowdsourcing
Yuchen Zhang, Xi Chen, Denny Zhou, and Michael I Jordan · 2014
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Peter Chin, Anup Rao, and Van Vu · 2015
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Achieving optimal misclassification proportion in stochastic block model
Chao Gao, Zongming Ma, Anderson Y Zhang, and Harrison H Zhou · 2015
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Tight bounds for learning a mixture of two gaussians
Moritz Hardt and Eric Price · 2015
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Fast community detection by score
Jiashun Jin et al · 2015
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Minimax rates of community detection in stochastic block models
Anderson Y Zhang and Harrison H Zhou · 2015
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Ten steps of em suffice for mixtures of two gaussians
Constantinos Daskalakis, Christos Tzamos, and Manolis Zampetakis · 2016
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Exact exponent in optimal rates for crowdsourcing
Chao Gao, Yu Lu, and Dengyong Zhou · 2016
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Community detection in degree-corrected block models
Chao Gao, Zongming Ma, Anderson Y Zhang, and Harrison H Zhou · 2016
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Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J. Wainwright, and Michael Jordan · 2016
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Statistical guarantees for estimating the centers of a two-component gaussian mixture by em
Jason M Klusowski and WD Brinda · 2016
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Global analysis of expectation maximization for mixtures of two gaussians
Ji Xu, Daniel Hsu, and Arian Maleki · 2016
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