Online, Semi-online, and Offline K-medians algorithms are
given. For both methods, the algorithms can be initialized
randomly or with the help of a robust hierarchical
clustering. The number of clusters can be selected with the
help of a penalized criterion. We provide functions to provide
robust clustering. Function gen_K() enables to generate a sample
of data following a contaminated Gaussian mixture.
Functions Kmedians() and Kmeans() consists in a K-median and a
K-means algorithms while Kplot() enables to produce graph for both
methods.
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Cardot, H. and Godichon-Baggioni, A. (2017). "Fast Estimation of the Median Covariation Matrix with Application to Online Robust Principal Components Analysis". Test, 26(3), 461-480 .
Godichon-Baggioni, A. and Surendran, S. "A penalized criterion for selecting the number of clusters for K-medians"
Vardi, Y. and Zhang, C.-H. (2000). "The multivariate L1-median and associated data depth". Proc. Natl. Acad. Sci. USA, 97(4):1423-1426. .