DDoutlier (version 0.1.0)

NOF: Natural Outlier Factor (NOF) algorithm

Description

Function to calculate the Natural Outlier Factor (NOF) as an outlier score for observations. Suggested by Huang, J., Zhu, Q., Yang, L. & Feng, J. (2015)

Usage

NOF(dataset)

Arguments

dataset

The dataset for which observations have a NOF score returned

Value

nb

A vector of in-degrees for observations

max_nb

Maximum in-degree observations in nb vector. Used as k-parameter in outlier detection of NOF

r

The natural neighbor eigenvalue

NOF

A vector of Natural Outlier Factor scores. The greater the NOF, the greater the outlierness

Details

NOF computes the nearest and reverse nearest neighborhood for observations, based on the natural neighborhood algorithm. Density is compared between observations and their neighbors. A kd-tree is used for kNN computation, using the kNN() function from the 'dbscan' package

References

Huang, J., Zhu, Q., Yang, L. & Feng, J. (2015). A non-parameter outlier detection algorithm based on Natural Neighbor. Knowledge-Based Systems. pp. 71-77. DOI: 10.1016/j.knosys.2015.10.014

Examples

# NOT RUN {
# Select dataset
X <- iris[,1:4]

# Run NOF algorithm
outlier_score <- NOF(dataset=X)$NOF

# Sort and find index for most outlying observations
names(outlier_score) <- 1:nrow(X)
sort(outlier_score, decreasing = TRUE)

# Inspect the distribution of outlier scores
hist(outlier_score)
# }