In many real-life applications, such as stock markets, network monitoring, and sensor networks, data are modeled as dynamic evolving time series which is continuous and unbounded in nature, and many such data streams concur usually. Clustering is useful in analyzing such paralleled data streams. This paper is interested in grouping these evolving data streams. For this purpose, a synopsis is maintained dynamically for each data stream. The construction of the synopsis is based on Discrete Wavelet Transform and utilizes the amnesic feature of data stream. By using the synopsis, a fast computation of approximate distances between streams and the cluster center can be implemented, and an efficient online version of the classical K-means clustering algorithm is developed. Experiments have proved the effectiveness of the proposed method.