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Profiling the clients' movement behaviors is useful for mobility modeling, anomaly detection, and location prediction. In this paper, we study clients' frequent and periodic movement patterns in a campus wireless network. We use offline data-mining algorithms to discover patterns from clients' association history, and analyze the reported patterns using statistical methods. Many of our results reflect the common characteristics of a typical academic campus, though we also observed some unusual association patterns. There are two challenges: one is to remove noise from data for efficient pattern discovery, and the other is to interpret discovered patterns. We address the first challenge using a heuristic-based approach applying domain knowledge. The second issue is harder to address because we do not have the knowledge of people's activities, but nonetheless we could make reasonable interpretation of the common patterns.
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Guanling Chen, Heng Huang, and Minkyong Kim, "Mining Frequent and Periodic Association Patterns." Dartmouth Computer Science Technical Report TR2005-550, July 2005.
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