Due to the popularity of Dynamic Adaptive Streaming Over HTTP (DASH), broadband and Internet service providers’ links transmit mainly multimedia content. As the most popular providers encrypt their video services, the attempts to identify their traffic through Deep Packet Inspection (DPI) encounter difficulties. Therefore, encrypted DASH traffic requires new classification methods. In this work, we propose to identify DASH traffic taking into account statistical dependencies among video flows. For this purpose, we employ cluster analysis which can identify groups of traffic flows that show similarity using only the application level information. In our work, we applied three unsupervised clustering algorithms, namely MinMax K-Means, OPTICS and AutoClass, to classify video traces obtained from an emulated environment. The experimental results show that the employed algorithms are able to effectively distinguish video flows generated by different play-out strategies. The classification performance depends on the network conditions and parameters of the learning process.