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A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data.

Goldstein M, Uchida S - PLoS ONE (2016)

Bottom Line: 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.Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time.As a conclusion, we give an advise on algorithm selection for typical real-world tasks.

View Article: PubMed Central - PubMed

Affiliation: Center for Co-Evolutional Social System Innovation, Kyushu University, Fukuoka, Japan.

ABSTRACT
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.

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Related in: MedlinePlus

A visualization of the results of the k-NN global anomaly detection algorithm.The anomaly score is represented by the bubble size whereas the color shows the labels of the artificially generated dataset.
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pone.0152173.g004: A visualization of the results of the k-NN global anomaly detection algorithm.The anomaly score is represented by the bubble size whereas the color shows the labels of the artificially generated dataset.

Mentions: In Fig 4 we exemplary illustrate how the result of an unsupervised anomaly detection algorithm (here: k-NN with k = 10) can be visualized. The plot was generated using a simple, artificially generated two-dimensional dataset with four Gaussian clusters and uniformly sampled anomalies. After applying the global k-NN, the outlier scores are visualized by the bubble-size of the corresponding instance. The color indicates the label, whereas anomalies are red. It can be seen, that k-NN cannot detect the anomalies close to the clusters well and assign small scores.


A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data.

Goldstein M, Uchida S - PLoS ONE (2016)

A visualization of the results of the k-NN global anomaly detection algorithm.The anomaly score is represented by the bubble size whereas the color shows the labels of the artificially generated dataset.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4836738&req=5

pone.0152173.g004: A visualization of the results of the k-NN global anomaly detection algorithm.The anomaly score is represented by the bubble size whereas the color shows the labels of the artificially generated dataset.
Mentions: In Fig 4 we exemplary illustrate how the result of an unsupervised anomaly detection algorithm (here: k-NN with k = 10) can be visualized. The plot was generated using a simple, artificially generated two-dimensional dataset with four Gaussian clusters and uniformly sampled anomalies. After applying the global k-NN, the outlier scores are visualized by the bubble-size of the corresponding instance. The color indicates the label, whereas anomalies are red. It can be seen, that k-NN cannot detect the anomalies close to the clusters well and assign small scores.

Bottom Line: 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.Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time.As a conclusion, we give an advise on algorithm selection for typical real-world tasks.

View Article: PubMed Central - PubMed

Affiliation: Center for Co-Evolutional Social System Innovation, Kyushu University, Fukuoka, Japan.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus