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.
With the rapid development of wireless networks, the potential of user behaviour analysis has brought up tremendous attention recently. The most common method for user clustering is by applying K-means on raw profile matrix. For example, the web browsing similarity among users of a university laboratory is examined using web log data in in @cite_4 . Besides, in @cite_2 , the authors evaluate the effectiveness of K-Means and spectral clustering and discuss the effect of the profile size to the clustering results. The authors in @cite_12 discuss this personalization problem from another perspective. They apply K-Means to construct user profiles using device-side traffic logs. The authors also point out that the extra information on gender may help with the problem in future work.
@cite_4 @cite_12 @cite_2
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As one of the most important tasks of Web Usage Mining (WUM), web user clustering, which establishes groups of users exhibiting similar browsing patterns, provides useful knowledge to personalized web services. In this paper, we cluster web users with KMeans algorithm based on web user log data. Given a set of web users and their associated historical web usage data, we study their behavior characteristic and cluster them. Experiment results show the feasibility and efficiency of such algorithm application. Web user clusters generated in this way can provide novel and useful knowledge for various personalized web applications. Mobile device can be used as a medium to send and receive the mobile internet content. However, there are several limitations using mobile internet. Content personalisation has been viewed as an important area when using mobile internet. In order for personalisation to be successful, understanding the user is important. In this paper, we explore the implementation of the user profile at client-side, which may be used whenever user connect to the mobile content provider. The client-side user profile can help to free the provider in performing analysis by using data mining technique at the mobile device. This research investigates the conceptual idea of using clustering and classification of user profile at the client-site mobile. In this paper, we applied K-means and compared several other classification algorithms like TwoStep, Kohenen and Anomaly to determine the boundaries of the important factors using information ranking separation. In this paper, we introduce and evaluate a framework for a user profile-based socializing application. We model the user profile as an unordered set composed of n keywords that represent users' preferences. We evaluate the effectiveness of two clustering algorithms, k-means and spectral clustering in the scope of social groups' assessment. Through experimental results we substantiate the applicability of spectral clustering in the examined service and we evaluate the impact of profile size in terms of the quality of partitions yielded by spectral clustering. The above are performed using case studies and scenarios developed in the context of 1ST project's MAGNET Beyond pilot services definition.