| .ktaucenters_run {ktaucenters} | R Documentation |
Robust Clustering algorithm based on centers, a robust and efficient version of kmeans.
Description
Robust Clustering algorithm based on centers, a robust and efficient version of kmeans.
Usage
.ktaucenters_run(x, centers, tolerance, max_iter)
Arguments
x |
numeric matrix of size n x p with all observations. |
centers |
numeric matrix with initial cluster centers. |
tolerance |
maximum difference between current and new computed clusters. Parameter used for the algorithm stopping rule. |
max_iter |
a maximum number of iterations used for the algorithm stopping rule. |
Value
A list with the following components:
tau |
|
iter |
number of iterations until convergence is achieved or maximum number of iteration is reached. |
di |
distance of each observation to its nearest cluster center. |
centers |
numeric matrix of size K x p, with the estimated K centers. |
clusters |
integer vector of size n with the cluster location for each observation. |
References
[1] Gonzalez, J. D., Yohai, V. J., & Zamar, R. H. (2019). Robust Clustering Using Tau-Scales. arXiv preprint arXiv:1906.08198.
[2] Maronna, R. A. and Yohai, V. J. (2017). Robust and efficient estimation of multivariate scatter and location.Computational Statistics &Data Analysis, 109 : 64–75.