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.