COF {DDoutlier}

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

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.

Value

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

Author(s)

Jacob H. Madsen

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

# 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)