knn: knn

In OutliersLearn: Educational Outlier Package with Common Outlier Detection Algorithms

knn

Description

This function implements the knn algorithm for outlier detection

Usage

knn(data, d, K, tutorialMode)

Arguments

data	Input Data (must be a data.frame)
d	Degree of outlier or distance at which an event is considered an outlier
K	Nearest neighbor for which an event must have a degree of outlier to be considered an outlier
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)
knn(inputData,3,2,FALSE) #Can be changed to TRUE

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