| Name : R-Kmedians
| |
| Version : 2.2.0
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp155.2.1
| Date : 2024-06-08 16:39:37
|
| Group : Development/Libraries/Other
| Source RPM : R-Kmedians-2.2.0-lp155.2.1.src.rpm
|
| Size : 0.06 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : K-Medians
|
Description :
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\" < 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>.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.5/x86_64 |
