With the popularity of wireless networks and the prevalence of personal mobile computing devices, understanding the characteristic of wireless network users is of great significance to the network performance. In this study, system logs from two universities, Dartmouth College and Shanghai Jiao Tong University(SJTU), were mined and analyzed. Every user's log was represented by a user profile. A novel weighted social similarity was proposed to quantify the resemblance of users considering influence of location visits. Based on the similarity, an unsupervised learning method was applied to cluster users. Though environment parameters are different, two universities both form many social groups with Pareto distribution of similarity and exponential distribution of group sizes. These findings are very important to the research of wireless network and social network .
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