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  "Title": "K-Medians",
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  "Authors@R": "c(person(\"Antoine\",\"Godichon-Baggioni\", role = c(\"aut\", \"cre\",\"cph\"),\nemail = \"antoine.godichon_baggioni@upmc.fr\"),\nperson(\"Sobihan\",\"Surendran\", role = \"aut\") )",
  "Description": "Online, Semi-online, and Offline K-medians algorithms are\ngiven. For both methods, the algorithms can be initialized\nrandomly or with the help of a robust hierarchical clustering.\nThe number of clusters can be selected with the help of a\npenalized criterion. We provide functions to provide robust\nclustering. Function gen_K() enables to generate a sample of\ndata following a contaminated Gaussian mixture. Functions\nKmedians() and Kmeans() consists in a K-median and a K-means\nalgorithms while Kplot() enables to produce graph for both\nmethods. Cardot, H., Cenac, P. and Zitt, P-A. (2013).\n\"Efficient and fast estimation of the geometric median in\nHilbert spaces with an averaged stochastic gradient algorithm\".\nBernoulli, 19, 18-43. <doi:10.3150/11-BEJ390>. Cardot, H. and\nGodichon-Baggioni, A. (2017). \"Fast Estimation of the Median\nCovariation Matrix with Application to Online Robust Principal\nComponents Analysis\". Test, 26(3), 461-480\n<doi:10.1007/s11749-016-0519-x>. Godichon-Baggioni, A. and\nSurendran, S. \"A penalized criterion for selecting the number\nof clusters for K-medians\" <arXiv:2209.03597> Vardi, Y. and\nZhang, C.-H. (2000). \"The multivariate L1-median and associated\ndata depth\". Proc. Natl. Acad. Sci. USA, 97(4):1423-1426.\n<doi:10.1073/pnas.97.4.1423>.",
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