Visualization of Anomalies Using Mixture Models

Anomaly detection is important to learn from major past events and to prepare for future crises. We propose a new anomaly detection method that visualizes multivariate data in a 2- or 3-dimensional space based on the probability of belonging to a mixture component and the probability of not belongin...

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Published in:Journal of intelligent manufacturing 2005-12, Vol.16 (6), p.635-643
Main Authors: Iwata, Tomoharu, Saito, Kazumi
Format: Article
Language:English
Subjects:
Data analysis
Economic statistics
Entropy
Manufacturing
Maximum likelihood method
Normal distribution
Probability
Studies
Time series
Visualization
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description Anomaly detection is important to learn from major past events and to prepare for future crises. We propose a new anomaly detection method that visualizes multivariate data in a 2- or 3-dimensional space based on the probability of belonging to a mixture component and the probability of not belonging to any components. It helps to visually understand not only the magnitude of anomalies but also the relationships among anomalous and normal samples. This may provide new knowledge in the data, since we can see it from a different viewpoint. We show the validity of the proposed method by using both an artificial and an economic time series. [PUBLICATION ABSTRACT]
doi_str_mv 10.1007/s10845-005-4367-x
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subjects Data analysis
Economic statistics
Entropy
Manufacturing
Maximum likelihood method
Normal distribution
Probability
Studies
Time series
Visualization
title Visualization of Anomalies Using Mixture Models
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