ldbod: Local Density-Based Outlier Detection

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.

AuthorKristopher Williams
Date of publication2016-12-22 09:04:34
MaintainerKristopher Williams <kristopher.williams83@gmail.com>
LicenseGPL-3
Version0.1.1
https://github.com/kwilliams83/ldbod

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Files in this package

ldbod
ldbod/NAMESPACE
ldbod/R
ldbod/R/ldbod.R ldbod/R/ldbod.ref.R
ldbod/README.md
ldbod/MD5
ldbod/DESCRIPTION
ldbod/man
ldbod/man/ldbod.ref.Rd ldbod/man/ldbod.Rd

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