Flexible procedures to compute local density-based outlier scores for ranking outliers. Both exact and approximate nearest neighbor search can be implemented, while also accommodating multiple neighborhood sizes and four different local density-based methods. It allows for referencing a random subsample of the input data or a user specified reference data set to compute outlier scores against, so both unsupervised and semi-supervised outlier detection can be implemented.
|Date of publication||2017-05-26 06:04:25 UTC|
|Maintainer||Kristopher Williams <email@example.com>|
|Package repository||View on CRAN|
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