NOF: Natural Outlier Factor (NOF) algorithm
In jhmadsen/DDoutlier: Distance & Density-Based Outlier Detection

Description Usage Arguments Details Value Author(s) References Examples

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)

1	NOF(dataset)

dataset

The dataset for which observations have a NOF score returned

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

`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

Jacob H. Madsen

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

# 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)