Description Usage Arguments Details Value Author(s) References Examples

Function to calculate Local Distance-based Outlier Factor (LDOF) as an outlier score for observations. Suggested by Zhang, K., Hutter, M. & Jin, H. (2009)

1 | ```
LDOF(dataset, k = 5)
``` |

`dataset` |
The dataset for which observations have an LDOF score returned |

`k` |
The number of nearest neighbors to compare distances with |

LDOF computes distance for an observations to its to k-nearest neighbors and compare the distance with the average distances between the nearest neighbors. The LDOF function is useful for outlier detection in clustering and other multidimensional domains

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

Jacob H. Madsen

Zhang, K., Hutter, M. & Jin, H. (2009). A New Local Distance-based Outlier Detection Approach for Scattered Real-World Data. Pacific-Asia Conference on Knowledge Discovery and Data Mining: Advances in Knowledge Discovery and Data Mining. pp. 813-822. DOI: 10.1007/978-3-642-01307-2_84

1 2 3 4 5 6 7 8 9 10 11 12 | ```
# Create dataset
X <- iris[,1:4]
# Find outliers by setting an optional range of k's
outlier_score <- LDOF(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)
``` |

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