Robust Clustering Procedures
Second derivative of the quasi function
Distance Matrix Computation
Flag outliers
Robust Clustering algorithm based on centers, a robust and efficient v...
improvedktaucenters
ktaucenters
ktaucentersfast
M scale
Normal Consistency Constants
Derivative of the quasi optimal function
Quasi optimal function
Robust Initialization based on Inverse Density estimator (ROBINDEN)
A clustering algorithm similar to K-Means is implemented, it has two main advantages, namely (a) The estimator is resistant to outliers, that means that results of estimator are still correct when there are atypical values in the sample and (b) The estimator is efficient, roughly speaking, if there are no outliers in the sample, results will be similar to those obtained by a classic algorithm (K-Means). Clustering procedure is carried out by minimizing the overall robust scale so-called tau scale. (see Gonzalez, Yohai and Zamar (2019) <arxiv:1906.08198>).