DDoutlier (version 0.1.0)

LDOF: Local Distance-based Outlier Factor (LDOF) algorithm

Description

Function to calculate Local Distance-based Outlier Factor (LDOF) as an outlier score for observations. Suggested by Zhang, K., Hutter, M. & Jin, H. (2009)

Usage

LDOF(dataset, k = 5)

Arguments

dataset

The dataset for which observations have an LDOF score returned

k

The number of nearest neighbors to compare distances with

Value

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

Details

LDOF computes distance for an observations to its to k-nearest neighbors and compare the distance with the average distances between the nearest neighbors. The LDOF function is useful for outlier detection in clustering and other multidimensional domains

References

Zhang, K., Hutter, M. & Jin, H. (2009). A New Local Distance-based Outlier Detection Approach for Scattered Real-World Data. Pacific-Asia Conference on Knowledge Discovery and Data Mining: Advances in Knowledge Discovery and Data Mining. pp. 813-822. DOI: 10.1007/978-3-642-01307-2_84

Examples

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

# Find outliers by setting an optional range of k's
outlier_score <- LDOF(dataset=X, k=10)

# 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)
# }