DDoutlier (version 0.1.0)

COF: Connectivity-based Outlier Factor (COF) algorithm

Description

Function to calculate the connectivity-based outlier factor as an outlier score for observations. Suggested by Tang, J., Chen, Z., Fu, A. W. C., & Cheung, D. W. (2002)

Usage

COF(dataset, k = 5)

Arguments

dataset

The dataset for which observations have a COF score returned

k

The number of k-nearest neighbors to construct a SBN-path with, being the number of neighbors for each observation to compare chaining-distance with. k has to be smaller than the number of observations in dataset

Value

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

Details

COF computes the connectivity-based outlier factor for observations, being the comparison of chaining-distances between observation subject to outlier scoring and neighboring observations. The COF function is useful for outlier detection in clustering and other multidimensional domains.

References

Tang, J., Chen, Z., Fu, A. W. C., & Cheung, D. W. (2002). Enhancing Effectiveness of Outlier Detections for Low Density Patterns. In Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD). Taipei. pp. 535-548. DOI: 10.1007/3-540-47887-6_53

Examples

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

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