lof: lof

In OutliersLearn: Educational Outlier Package with Common Outlier Detection Algorithms

lof

Description

Local Outlier Factor algorithm to detect outliers

Usage

lof(inputData, K, threshold, tutorialMode)

Arguments

inputData	Input Data (must be a data.frame)
K	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.
threshold	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)
tutorialMode	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)

Value

None, does not return any value

Author(s)

Andres Missiego Manjon

Examples

inputData = t(matrix(c(3,2,3.5,12,4.7,4.1,5.2,
4.9,7.1,6.1,6.2,5.2,14,5.3),2,7,dimnames=list(c("r","d"))));
inputData = data.frame(inputData);
lof(inputData,3,0.5,FALSE) #Can be changed to TRUE

OutliersLearn documentation built on June 22, 2024, 10:23 a.m.