Angle-Based Outlier Detection

Performs angle-based outlier detection on a given dataframe. Three methods are available, a full but slow implementation using all the data that has cubic complexity, a fully randomized one which is way more efficient and another using k-nearest neighbours. These algorithms are specially well suited for high dimensional data outlier detection.


Implementation of the angle-based outlier factor in R. Three methods are available, a full but slow implementation using all the data that has cubic complexity, a fully randomized one which is way more efficient and another using k-nearest neighbours. These algorithms are specially well suited for high dimensional data outlier detection.

Install

The package is available on CRAN:

install.packages("abodOutlier")
library(abodOutlier)

Usage

abod(faithful, method = "randomized", n_sample_size = 30)
abod(faithful, method = "knn", k = 20)

MIT Licensed.

News

Reference manual

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install.packages("abodOutlier")

0.1 by Jose Jimenez, 3 years ago


Browse source code at https://github.com/cran/abodOutlier


Authors: Jose Jimenez <[email protected]>


Documentation:   PDF Manual  


MIT + file LICENSE license


Depends on cluster


Suggested by stranger.


See at CRAN