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
Browse package contents
Package details |
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| Author | Kristopher Williams |
| Maintainer | Kristopher Williams <kristopher.williams83@gmail.com> |
| License | GPL-3 |
| Version | 0.1.2 |
| URL | https://github.com/kwilliams83/ldbod |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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