ldbod: Local Density-Based Outlier Detection

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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 k values and four different local density-based methods. It allows for referencing a random subsample of 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.

Author
Kristopher Williams
Date of publication
2016-09-14 18:50:59
Maintainer
Kristopher Williams <kristopher.williams83@gmail.com>
License
GPL-3
Version
0.1.0
URLs

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Man pages

ldbod
Local Density-Based Outlier Detection using Subsampling with...
ldbod.ref
Local Density-Based Outlier Detection using Reference Data...

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