Kmedians

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. Cardot, H., Cenac, P. and Zitt, P-A. (2013). "Efficient and fast estimation of the geometric median in Hilbert spaces with an averaged stochastic gradient algorithm". Bernoulli, 19, 18-43. doi:10.3150/11-BEJ390. 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 doi:10.1007/s11749-016-0519-x. Godichon-Baggioni, A. and Surendran, S. "A penalized criterion for selecting the number of clusters for K-medians" doi:10.48550/arXiv.2209.03597 Vardi, Y. and Zhang, C.-H. (2000). "The multivariate L1-median and associated data depth". Proc. Natl. Acad. Sci. USA, 97(4):1423-1426. doi:10.1073/pnas.97.4.1423.

Last 30 days

Last 365 days

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.