A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data

Markus Goldstein, Seiichi Uchida

Research output: Contribution to journalArticlepeer-review

596 Citations (Scopus)


Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised anomaly detection and is addressed in many practical applications, for example in network intrusion detection, fraud detection as well as in the life science and medical domain. Dozens of algorithms have been proposed in this area, but unfortunately the research community still lacks a comparative universal evaluation as well as common publicly available datasets. These shortcomings are addressed in this study, where 19 different unsupervised anomaly detection algorithms are evaluated on 10 different datasets from multiple application domains. By publishing the source code and the datasets, this paper aims to be a new well-funded basis for unsupervised anomaly detection research. Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time. Besides the anomaly detection performance, computational effort, the impact of parameter settings as well as the global/local anomaly detection behavior is outlined. As a conclusion, we give an advise on algorithm selection for typical real-world tasks.

Original languageEnglish
Article numbere0152173
JournalPloS one
Issue number4
Publication statusPublished - Apr 2016

All Science Journal Classification (ASJC) codes

  • General Biochemistry,Genetics and Molecular Biology
  • General Agricultural and Biological Sciences


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