Local Outlier Factor algorithm to detect outliers

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 

lof function

<dl><dt>inputData</dt>
<dd>Input Data (must be a data.frame)</dd>
<dt>K</dt>
<dd>This number represents the nearest neighbor to use to calculate the density of each point. This value is chosen arbitrarily and is responsibility of the data scientist/user to select a number adequate to the dataset.</dd>
<dt>threshold</dt>
<dd>Value that is used to classify the points comparing it to the calculated ARDs of the points in the dataset. If the ARD is smaller, the point is classified as an outliers. If not, the point is classified as a normal point (inlier)</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

lof — lof

<dl>

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


<dt>K</dt>
<dd>This number represents the nearest neighbor to use to calculate the density of each point. This value is chosen arbitrarily and is responsibility of the data scientist/user to select a number adequate to the dataset.</dd>


<dt>threshold</dt>
<dd>Value that is used to classify the points comparing it to the calculated ARDs of the points in the dataset. If the ARD is smaller, the point is classified as an outliers. If not, the point is classified as a normal point (inlier)</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>

lof: lof

Description

Usage

Value

Arguments

Author

Examples