Mining Behavioral Groups Based on Usage Data in Large Wireless LANs
Wireless networks and personalized mobile devices are deeply integrated and embedded in the lives. Such wide adoptions of new technologies will impact user behavior and in turn will affect network performance. It is imperative to characterize the fundamental structure of wireless user behavior in order to model, manage, leverage and design efficient mobile networks. One major challenge in characterizing user behavior stems from the significant size and complexity of user behavioral data. Without summarization and dimension reduction, the sheer amount of data does not provide much useful information. The key contribution of the paper is a novel similarity metric based on a matrix representation of mobility preferences and its decomposition.