kwilliams83/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.

Getting started

Package details

AuthorKristopher Williams
MaintainerKristopher Williams <kristopher.williams83@gmail.com>
LicenseGPL-3
Version0.1.2
URL https://github.com/kwilliams83/ldbod
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("kwilliams83/ldbod")
kwilliams83/ldbod documentation built on May 20, 2019, 7:27 p.m.