A subscriber classification approach for mobile cellular networks

The classification of subscriber types in mobile cellular networks is valuable for network service providers since it provides a mechanism to plan network services by better understanding subscriber behaviour in a network. Mobile networks contain vast repositories of data that store valuable information regarding subscriber behaviour. In this paper, a new approach for subscriber classification in mobile cellular networks is proposed. The proposed approach considers network traffic generated from a mobile cellular network operator in South Africa. The proposed approach makes use of a difference histogram approach for feature extraction and a fuzzy c-means clustering algorithm to classify traffic data into subscriber classes. To validate the proposed approach, a comparative analysis of two different multi-resolution feature extraction approaches, the empirical mode decomposition (EMD) approach and the discrete wavelet packet transform (DWPT) are compared with results obtained with the difference histogram (DH) approach. It is shown that the difference histogram provides better clustering results when compared to the two multi-resolution approaches demonstrating the potential of the difference histogram approach.