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ldbod

Local Density-Based Outlier Detection

Installation

About

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.

github.com/kwilliams83/ldbod

Key Metrics

Version 0.1.2
R ≥ 3.2.0
Published 2017-05-26 2535 days ago
Needs compilation? no
License GPL-3
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Maintainer

Maintainer

Kristopher Williams

kristopher.williams83@gmail.com

Authors

Kristopher Williams

Material

README
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

ldbod archive

Depends

R ≥ 3.2.0

Imports

stats
RANN
mnormt