In-degree for observations in a k-nearest neighbors graph
Function to calculate in-degree as an outlier score for observations, given a k-nearest neighbors graph. Suggested by Hautamaki, V., & Ismo, K. (2004)
KNN_IN(dataset, k = 5)
dataset |
The dataset for which observations have an in-degree returned |
k |
The number of k-nearest neighbors to construct a graph with. Has to be smaller than the number of observations in dataset |
KNN_IN computes the in-degree, being the number of reverse neighbors. For computing the in-degree, a k-nearest neighbors graph is computed. A kd-tree is used for kNN computation, using the kNN() function from the 'dbscan' package. The KNN_IN function is useful for outlier detection in clustering and other multidimensional domains.
A vector of in-degree for observations. The smaller the in-degree, the greater outlierness
Jacob H. Madsen
Hautamaki, V., & Ismo, K. (2004). Outlier Detection Using k-Nearest Neighbour Graph. In International Conference on Pattern Recognition. Cambridge, UK. pp. 430-433. DOI: 10.1109/ICPR.2004.1334558
# Create dataset X <- iris[,1:4] # Find outliers by setting an optional k outlier_score <- KNN_IN(dataset=X, k=10) # Sort and find index for most outlying observations names(outlier_score) <- 1:nrow(X) sort(outlier_score, decreasing = FALSE) # Inspect the distribution of outlier scores hist(outlier_score)
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