KDEOS: Kernel Density Estimation Outlier Score (KDEOS) algorithm...
In DDoutlier: Distance & Density-Based Outlier Detection

Description Usage Arguments Details Value Author(s) References Examples

Function to calculate a density estimation as an outlier score for observations, over a range of k-nearest neighbors. Suggested by Schubert, E., Zimek, A. & Kriegel, H-P. (2014)

1	KDEOS(dataset, k_min = 5, k_max = 10, eps = NULL)

`dataset`	The dataset for which observations have an KDEOS score returned
`k_min`	The k parameter starting the k-range
`k_max`	The k parameter ending the k-range. Has to be smaller than the number of observations in dataset and greater than or equal to k_min
`eps`	An optional minimum bandwidth. If eps is smaller than the mean reachability distance for observations, eps is used. Otherwise mean reachability distance is used as bandwidth

KDEOS computes a kernel density estimation over a user-given range of k-nearest neighbors. The score is normalized between 0 and 1, such that observation with 1 has the lowest density estimation and greatest outlierness. A gaussian kernel is used for estimation with a bandwidth being the reachability distance for neighboring observations. If a lower user-given bandwidth is desired, putting more weight on outlying observations, eps has to be lower than the mean reachability distance for observations. A kd-tree is used for kNN computation, using the kNN() function from the 'dbscan' package. The KDEOS function is useful for outlier detection in clustering and other multidimensional domains

A vector of KDEOS scores normalized between 1 and 0, with 1 being the greatest outlierness

Jacob H. Madsen

Schubert, E., Zimek, A. & Kriegel, H-P. (2014). Generalized Outlier Detection with Flexible Kernel Density Estimates. Proceedings of the 2014 SIAM International Conference on Data Mining. Philadelphia, USA. pp. 542-550. DOI: 10.1137/1.9781611973440.63

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

# Find outliers by setting an optional range of k's
outlier_score <- KDEOS(dataset=X, k_min=10, k_max=15)

# 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)