K-means based clustering on mobile usage for social network analysis purpose

The development of mobile network technology provides a great potential for social networking services. This paper studied data mining for social network analysis purpose, which aims at find people´s social network patterns by analyzing the information about their mobile phone usage. In this research, the real database of MIT´s Reality Mining project is employed. The classification model presented in this project provides a new approach to find the proximity between users - based on their registration frequencies to specific cellular towers associated their working places. K-means Algorithm is applied for clustering, and we find the result could achieve the highest accuracy 0.823 at the number groups k = 6. The clustering result successfully reflects the higher proximity (at work) for the intra-class subjects.