  Outlier detection refers to the identification of data points that deviate
from a general data distribution. Existing unsupervised approaches often suffer
from high computational cost, complex hyperparameter tuning, and limited
interpretability, especially when working with large, high-dimensional
datasets. To address these issues, we present a simple yet effective algorithm
called ECOD (Empirical-Cumulative-distribution-based Outlier Detection), which
is inspired by the fact that outliers are often the "rare events" that appear
in the tails of a distribution. In a nutshell, ECOD first estimates the
underlying distribution of the input data in a nonparametric fashion by
computing the empirical cumulative distribution per dimension of the data. ECOD
then uses these empirical distributions to estimate tail probabilities per
dimension for each data point. Finally, ECOD computes an outlier score of each
data point by aggregating estimated tail probabilities across dimensions. Our
contributions are as follows: (1) we propose a novel outlier detection method
called ECOD, which is both parameter-free and easy to interpret; (2) we perform
extensive experiments on 30 benchmark datasets, where we find that ECOD
outperforms 11 state-of-the-art baselines in terms of accuracy, efficiency, and
scalability; and (3) we release an easy-to-use and scalable (with distributed
support) Python implementation for accessibility and reproducibility.
