  In this study we evaluate 32 unsupervised anomaly detection algorithms on 52
real-world multivariate tabular datasets, performing the largest comparison of
unsupervised anomaly detection algorithms to date. On this collection of
datasets, the $k$-thNN (distance to the $k$-nearest neighbor) algorithm
significantly outperforms the most other algorithms. Visualizing and then
clustering the relative performance of the considered algorithms on all
datasets, we identify two clear clusters: one with ``local'' datasets, and
another with ``global'' datasets. ``Local'' anomalies occupy a region with low
density when compared to nearby samples, while ``global'' occupy an overall low
density region in the feature space. On the local datasets the $k$NN
($k$-nearest neighbor) algorithm comes out on top. On the global datasets, the
EIF (extended isolation forest) algorithm performs the best. Also taking into
consideration the algorithms' computational complexity, a toolbox with these
three unsupervised anomaly detection algorithms suffices for finding anomalies
in this representative collection of multivariate datasets. By providing access
to code and datasets, our study can be easily reproduced and extended with more
algorithms and/or datasets.
