  User behaviour analysis based on traffic log in wireless networks can be
beneficial to many fields in real life: not only for commercial purposes, but
also for improving network service quality and social management. We cluster
users into groups marked by the most frequently visited websites to find their
preferences. In this paper, we propose a user behaviour model based on Topic
Model from document classification problems. We use the logarithmic TF-IDF
(term frequency - inverse document frequency) weighing to form a
high-dimensional sparse feature matrix. Then we apply LSA (Latent semantic
analysis) to deduce the latent topic distribution and generate a
low-dimensional dense feature matrix. K-means++, which is a classic clustering
algorithm, is then applied to the dense feature matrix and several
interpretable user clusters are found. Moreover, by combining the clustering
results with additional demographical information, including age, gender, and
financial information, we are able to uncover more realistic implications from
the clustering results.
