Exploring communication and mobility behavior of 3G network users and its temporal consistency

Over the past decade, telecommunication network operators have more and more realized the added value of data analytics for their network deployment efficiency. Early studies targeted the global network perspective by localizing peak loads, both in terms of area and time period. Due to their higher granularity and information richness, current telecommunication datasets allow increasingly deeper insights into the network activities of the users. Existing network traffic classification studies tend to divide users into groups without considering the transitions between different groups caused by individual behavioral traits, which we expect to show observable regularities. Our approach defines a profiling model that characterizes the user behavior as well as its temporal dynamics from two perspectives: w.r.t. (i) the network load the users generate, and (ii) their mobility patterns. The model is evaluated with two unsupervised clustering algorithms of different complexity (namely, XMeans and EM) by means of a 3G trace dataset from a European operator.