| LDOF {DDoutlier} | R Documentation |
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 |
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
Value
A vector of LDOF scores for observations. The greater the LDOF score, the greater outlierness
Author(s)
Jacob H. Madsen
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
# 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)