LOF {DDoutlier}

Local Outlier Factor (LOF) algorithm

Description

Function to calculate the Local Outlier Factor (LOF) as an outlier score for observations. Suggested by Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000)

Usage

LOF(dataset, k = 5)

Arguments

dataset

The dataset for which observations have an LOF score returned

k

The number of k-nearest neighbors to compare density with. k has to be smaller than number of observations in dataset

Details

LOF computes a local density 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 factor. A kd-tree is used for kNN computation, using the kNN() function from the 'dbscan' package. The LOF function is useful for outlier detection in clustering and other multidimensional domains

Value

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

Author(s)

Jacob H. Madsen

References

Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: Identifying Density-Based Local Outliers. In Int. Conf. On Management of Data. Dallas, TX. pp. 93-104. DOI: 10.1145/342009.335388

Examples

# Create dataset
X <- iris[,1:4]

# Find outliers by setting an optional k
outlier_score <- LOF(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)