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

1 2 3 4 5 6 7 8 9 10 11 12 | ```
# 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)
``` |

DDoutlier documentation built on May 1, 2019, 10:20 p.m.

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