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Abstract. 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 mul-tivariate data. Two overlapping windows are used to quantify the changes. While a win-dow 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. 1