nnk: nnk

In pguIMP: 'pguIMP'

Description

Outlier detection using kth Nearest Neighbour Distance method Takes a dataset and finds its outliers using distance-based method

Usage

nnk(
  x,
  k = 0.05 * nrow(x),
  cutoff = 0.95,
  Method = "euclidean",
  rnames = FALSE,
  boottimes = 100
)

Arguments

x	dataset for which outliers are to be found
k	No. of nearest neighbours to be used, default value is 0.05*nrow(x)
cutoff	Percentile threshold used for distance, default value is 0.95
Method	Distance method, default is Euclidean
rnames	Logical value indicating whether the dataset has rownames, default value is False
boottimes	Number of bootsrap samples to find the cutoff, default is 100 samples

Details

nnk computes kth nearest neighbour distance of an observation and based on the bootstrapped cutoff, labels an observation as outlier. Outlierliness of the labelled 'Outlier' is also reported and it is the bootstrap estimate of probability of the observation being an outlier. For bivariate data, it also shows the scatterplot of the data with labelled outliers.

Value

Outlier Observations: A matrix of outlier observations

Location of Outlier: Vector of Sr. no. of outliers

Outlier probability: Vector of proportion of times an outlier exceeds local bootstrap cutof

Author(s)

Vinay Tiwari, Akanksha Kashikar

References

Hautamaki, V., Karkkainen, I., and Franti, P. 2004. Outlier detection using k-nearest neighbour graph. In Proc. IEEE Int. Conf. on Pattern Recognition (ICPR), Cambridge, UK.

Examples

#Create dataset
X=iris[,1:4]
#Outlier detection
nnk(X,k=4)

pguIMP documentation built on Sept. 30, 2021, 5:08 p.m.