  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.
