This function implements the knn algorithm for outlier detection

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 

knn function

<dl><dt>data</dt>
<dd>Input Data (must be a data.frame)</dd>
<dt>d</dt>
<dd>Degree of outlier or distance at which an event is considered an outlier</dd>
<dt>K</dt>
<dd>Nearest neighbor for which an event must have a degree of outlier to be considered an outlier</dd>
<dt>learn</dt>
<dd>if TRUE the tutorial mode is activated (the algorithm will include an explanation detailing the theory behind the outlier detection algorithm and a step by step explanation of how is the data processed to obtain the outliers following the theory mentioned earlier)</dd></dl>

Arguments

Author

knn — knn

<dl>

<dt>data</dt>
<dd>Input Data (must be a data.frame)</dd>


<dt>d</dt>
<dd>Degree of outlier or distance at which an event is considered an outlier</dd>


<dt>K</dt>
<dd>Nearest neighbor for which an event must have a degree of outlier to be considered an outlier</dd>


<dt>learn</dt>
<dd>if TRUE the tutorial mode is activated (the algorithm will include an explanation detailing the theory behind the outlier detection algorithm and a step by step explanation of how is the data processed to obtain the outliers following the theory mentioned earlier)</dd>

</dl>

knn: knn

Description

Usage

Value

Arguments

Author

Examples