DDoutlier (version 0.1.0)

LOOP: Local Outlier Probability (LOOP) algorithm

Description

Function to calculate the Local Outlier Probability (LOOP) as an outlier score for observations. Suggested by Kriegel, H.-P., Kr<U+00F6>ger, P., Schubert, E., & Zimek, A. (2009)

Usage

LOOP(dataset, k = 5, lambda = 3)

Arguments

dataset

The dataset for which observations have a LOOP score returned

k

The number of k-nearest neighbors to compare density with

lambda

Multiplication factor for standard deviation. The greater lambda, the smoother results. Default is 3 as used in original papers experiments

Value

A vector of LOOP scores for observations. 1 indicates outlierness and 0 indicate inlierness

Details

LOOP computes a local density based on probabilistic set distance for observations, with a user-given k-nearest neighbors. The density is compared to the density of the respective nearest neighbors, resulting in the local outlier probability. The values ranges from 0 to 1, with 1 being the greatest outlierness. A kd-tree is used for kNN computation, using the kNN() function from the 'dbscan' package. The LOOP function is useful for outlier detection in clustering and other multidimensional domains

References

Kriegel, H.-P., Kr<U+00F6>ger, P., Schubert, E., & Zimek, A. (2009). LoOP: Local Outlier Probabilities. In ACM Conference on Information and Knowledge Management, CIKM 2009, Hong Kong, China. pp. 1649-1652. DOI: 10.1145/1645953.1646195

Examples

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

# Find outliers by setting an optional k
outlier_score <- LOOP(dataset=X, k=10, lambda=3)

# 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)
# }