Many automated systems need the capability of automatic change detection without the given detection threshold. This paper presents an automated change detection algorithm in streaming multivariate data. Two overlapping windows are used to quantify the changes. While a window is used as the reference window from which the clustering is created, the other called the current window captures the newly incoming data points. A newly incoming data point can be considered a change point if it is not a member of any cluster. As our clustering-based change detector does not require detection threshold, it is an automated detector. Based on this change detector, we propose a reactive clustering algorithm for streaming data. Our empirical results show that, our clustering-based change detector works well with multivariate streaming data. The detection accuracy depends on the number of clusters in the reference window, the window width.
Automated systems should be capable of automatically detecting the changes without the given detection threshold. Some change detection methods can automatically tune the detection thresholds so that the rate of false alarms is not greater than a given rate of false alarms. Gustafson and Palmquist deal with the problem of automated tuning of change detectors with given false alarm rate @cite_14 . Their approach computes the detection threshold by estimating a parametric distribution. The advantage of this method is that they can predict detection threshold with no or few false alarms from the used data. However, it is parametric method.
@cite_14
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This contribution addresses the problem of automated tuning of change detectors with given false alarm rate. By estimating a parametric distribution to the test statistics computed from real or simulated data, the threshold of the test can be computed directly. The advantage is that we can predict the threshold although there are no or very few false alarms in the used data. Using real data, the method is robust to assumed noise distributions and modeling errors. We illustrate the method on the CUSUM and GLR tests applied to friction estimation in cars and an airborne navigation system, respectively.