.distance

Computes and returns the distance matrix using euclidean distance
measure to compute the distances between the rows of a
data matrix.

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>).

Juan Domingo Gonzalez

ktaucenters

Robust Clustering Procedures

Victor J. Yohai

Ruben H. Zamar

Douglas Alberto Carmona Guanipa

.distance function

<dl><dt>x</dt>
<dd>a numeric matrix.</dd></dl>

Arguments

Distance Matrix Computation — .distance

<dl>

<dt>x</dt>
<dd>a numeric matrix.</dd>

</dl>

.distance: Distance Matrix Computation

Description

Usage

Value

Arguments