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To increase the scalability of cloud computing, utilizing resources of individual users has been widely adopted especially in video streaming services. Accurately predicting behavior of user nodes is critical to achieve a high efficiency in such a peer-assisted system. Though there have been many measurement studies on peer-to-peer systems, most of them have focused on the design and characterization of the systems. Thus the behavior patterns of individual nodes have seldom been studied. In this paper, we present new techniques for classifying behavior of nodes in terms of availability and compare them with naive manual classification. We apply a k-means clustering algorithm with various classification criteria on real trace data of a peer-to-peer system. Our analysis shows that there are three dominant time zones with respect to the availability peak time. Our study will give a useful hint to a system designer in handling churns more efficiently based on the peer classification.