quantile_outliersLearn

Function that obtains the 'v' quantile

Provides implementations of some of the most important outlier detection algorithms.
Includes a tutorial mode option that shows a description of each algorithm and provides
a step-by-step execution explanation of how it identifies outliers from the given data
with the specified input parameters. References include the works of Azzedine Boukerche,
Lining Zheng, and Omar Alfandi (2020) <doi:10.1145/3381028>, Abir Smiti (2020)
<doi:10.1016/j.cosrev.2020.100306>, and Xiaogang Su, Chih-Ling Tsai (2011)
<doi:10.1002/widm.19>.

Andriy Protsak Protsak

UAHDataScienceO

Educational Outlier Detection Algorithms with Step-by-Step
Tutorials

Andres Missiego Manjon

Juan Jose Cuadrado Gallego

Universidad de Alcala de Henares 

quantile_outliersLearn function

<dl><dt>data</dt>
<dd>Input Data</dd>
<dt>v</dt>
<dd>Goes from 0 to 1 (e.g. 0.25). Indicates the quantile that wants to be obtained</dd></dl>

Arguments

Author

quantile_outliersLearn — quantile_outliersLearn

<dl>

<dt>data</dt>
<dd>Input Data</dd>


<dt>v</dt>
<dd>Goes from 0 to 1 (e.g. 0.25). Indicates the quantile that wants to be obtained</dd>

</dl>

quantile_outliersLearn: quantile_outliersLearn

Description

Usage

Value

Arguments

Author

Examples