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Clustering is a popular form of unsupervised learning for geometric data.
Sur la division des corp materiels en parties
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David Arthur and Sergei Vassilvitskii · 2007
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The hardness of k-means clustering
Sanjoy Dasgupta · 2008
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Hinrich Schütze, Christopher D Manning, and Prabhakar Raghavan · 2008
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Adaptive sampling for k-means clustering
Ankit Aggarwal, Amit Deshpande, and Ravi Kannan · 2009
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NP-hardness of Euclidean sum-of-squares clustering
Daniel Aloise, Amit Deshpande, Pierre Hansen, and Preyas Popat · 2009
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Unsupervised feature selection for the k-means clustering problem
Christos Boutsidis, Petros Drineas, and Michael W Mahoney · 2009
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How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert MÞller · 2010
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Interpretable clustering using unsupervised binary trees
Ricardo Fraiman, Badih Ghattas, and Marcela Svarc · 2013
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The effectiveness of Lloyd-type methods for the k-means problem
Rafail Ostrovsky, Yuval Rabani, Leonard J Schulman, and Chaitanya Swamy · 2013
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The hardness of approximation of Euclidean k-means
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Dimensionality reduction for k-means clustering and low rank approximation
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Same-cluster querying for overlapping clusters
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From clustering to cluster explanations via neural networks
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Fair k-center clustering for data summarization
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The mythos of model interpretability
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Performance of Johnson-Lindenstrauss transform for k-means and k-medians clustering
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Interpretable Machine Learning
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Unexpected effects of online k-means clustering
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
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