INFLO: Influenced Outlierness (INFLO) algorithm
In DDoutlier: Distance & Density-Based Outlier Detection

Description Usage Arguments Details Value Author(s) References Examples

Function to calculate the influenced outlierness as an outlier score for observations. Suggested by Jin, W., Tung, A. K. H., Han, J., & Wang, W. (2006)

1	INFLO(dataset, k = 5)

`dataset`	The dataset for which observations have an INFLO score returned
`k`	The number of reverse k-nearest neighbors to compare density with. k has to be smaller than the number of observations in dataset

INFLO computes the influenced outlierness score for observations, being the comparison of density in neighborhood of observation subject to outlier scoring and density in the reverse neighborhood. A kd-tree is used for kNN computation, using the kNN() function from the 'dbscan' package. The INFLO function is useful for outlier detection in clustering and other multidimensional domains

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

Jacob H. Madsen

Jin, W., Tung, A. K. H., Han, J., & Wang, W. (2006). Ranking Outliers Using Symmetric Neighborhood Relationship. In Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD). Singapore. pp 577-593. DOI: 10.1007/11731139_68

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

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