DDoutlier (version 0.1.0)

RKOF: Robust Kernel-based Outlier Factor (RKOF) algorithm with gaussian kernel

Description

Function to to calculate the RKOF score for observations as suggested by Gao, J., Hu, W., Zhang, X. & Wu, Ou. (2011)

Usage

RKOF(dataset, k = 5, C = 1, alpha = 1, sigma2 = 1)

Arguments

dataset

The dataset for which observations have an RKOF score returned

k

The number of nearest neighbors to compare density estimation with

C

Multiplication parameter for k-distance of neighboring observations. Act as bandwidth increaser. Default is 1 such that k-distance is used for the gaussian kernel

alpha

Sensivity parameter for k-distance/bandwidth. Small alpha creates small variance in RKOF and vice versa. Default is 1

sigma2

Variance parameter for weighting of neighboring observations

Value

A vector of RKOF scores for observations. The greater the RKOF score, the greater outlierness

Details

RKOF computes a kernel density estimation by comparing density estimation to the density of neighboring observations. A gaussian kernel is used for density estimation, given a bandwidth with k-distance. K-distance can be influenced with the parameters C and alpha. A kd-tree is used for kNN computation, using the kNN() function from the 'dbscan' package. The RKOF function is useful for outlier detection in clustering and other multidimensional domains

References

Gao, J., Hu, W., Zhang, X. & Wu, Ou. (2011). RKOF: Robust Kernel-Based Local Outlier Detection. Pacific-Asia Conference on Knowledge Discovery and Data Mining: Advances in Knowledge Discovery and Data Mining. pp. 270-283. DOI: 10.1007/978-3-642-20847-8_23

Examples

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

# Find outliers by setting an optional k
outlier_score <- RKOF(dataset=X, k = 10, C = 1, alpha = 1, sigma2 = 1)

# 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)
# }