We survey unsupervised machine learning algorithms in the context of outlier detection. This task challenges state-of-the-art methods from a variety of research fields to applications including fraud detection, intrusion detection, medical diagnoses and data cleaning. The selected methods are benchmarked on publicly available datasets and novel industrial datasets. Each method is then submitted to extensive scalability, memory consumption and robustness tests in order to build a full overview of the algorithms' characteristics.
A comparative evaluation of outlier detection algorithms: experiments and analyses
Pattern Recognition, Volume 74, February 2018
© Elsevier. Personal use of this material is permitted. The definitive version of this paper was published in Pattern Recognition, Volume 74, February 2018 and is available at : http://doi.org/10.1016/j.patcog.2017.09.037
PERMALINK : https://www.eurecom.fr/publication/5334