A comparative evaluation of outlier detection algorithms: Experiments and analyses

•Experimental comparison and analysis of unsupervised outlier detection techniques.•Based on ROC, precision-recall, computation time, memory usage and robustness.•Extend a nonparametric Bayesian method to model numerical and categorical features.•Experiments make use of novel industrial datasets and assess generalization abilities. 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.