knn {OutliersLearn} | R Documentation |
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
[Package OutliersLearn version 1.0.0 Index]