DDoutlier (version 0.1.0)

LDF: Local Density Factor (LDF) algorithm with gaussian kernel

Description

Function to calculate a Local Density Estimate (LDE) and Local Density Factor (LDF), as an outlier score, with a gaussian kernel. Suggested by Latecki, L., Lazarevic, A. & Prokrajac, D. (2007)

Usage

LDF(dataset, k = 5, h = 1, c = 1)

Arguments

dataset

The dataset for which observations have an LDE and LDF score returned

k

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

h

User-given bandwidth for kernel functions. The greater the bandwidth, the smoother kernels and lesser weight are put on outliers. Default is 1

c

Scaling constant for comparison of LDE to neighboring observations. LDF is the comparison of average LDE for an observation and its neighboring observations. Thus, c=1 gives results in an LDF between 0 and 1, while c=0 can result in very large or infinite values of LDF. Default is 1

Value

LDE

A vector of Local Density Estimate for observations. The greater the LDE, the greater centrality

LDF

A vector of Local Density Factor for observations. The greater the LDF, the greater the outlierness

Details

LDF computes a kernel density estimation, called LDE, over a user-given number of k-nearest neighbors. The LDF score is the comparison of Local Density Estimate (LDE) for an observation to its neighboring observations. Naturally, if an observation has a greater LDE than its neighboring observations, it has no outlierness whereas an observation with smaller LDE than its neighboring observations has great outlierness. A kd-tree is used for kNN computation, using the kNN() function from the 'dbscan' package. The LDF function is useful for outlier detection in clustering and other multidimensional domains

References

Latecki, L., Lazarevic, A. & Prokrajac, D. (2007). Outlier Detection with Kernel Density Functions. International Workshop on Machine Learning and Data Mining in Pattern Recognition: Machine Learning and Data Mining in Pattern Recognition. pp. 61-75. DOI: 10.1007/978-3-540-73499-4_6

Examples

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

# Find outliers by setting an optional range of k's
outlier_score <- LDF(dataset=X, k=10, h=2, c=1)$LDF

# 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)
# }