Mobile communication has become a vital part of modern communication. The cost of network infrastructure has become a deciding factor with rise in mobile phone usage. Subscriber mobility patterns have major effect on load of radio cell in the network. The need for data analysis of subscriber mobility data is of utmost priority.

The paper aims at classifying the entire dataset provided by Telenor, into two main groups i.e. Infrastructure stressing and Infrastructure friendly with respect to their impact on the mobile network. The research aims to predict the behavior of new subscriber based on his MOSAIC group.

A heuristic method is formulated to characterize the subscribers into three different segments based on their mobility. Tetris Optimization is used to reveal the “Infrastructure Stressing” subscribers in the mobile network. All the experiments have been conducted on the subscriber trajectory data provided by the telecom operator.

The results from the experimentation reveal that 5 percent of subscribers from entire data set are “Infrastructure Stressing”. A classification model is developed and evaluated to label the new subscriber as friendly or stressing using WEKA machine learning tool. Naïve Bayes, k-nearest neighbor and J48 Decision tree are classification algorithms used to train the model and to find the relation between features in the labeled subscriber dataset