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.
|Author||Jose Jimenez <firstname.lastname@example.org>|
|Date of publication||2015-08-31 14:31:42|
|Maintainer||Jose Jimenez <email@example.com>|
|License||MIT + file LICENSE|