Analysis Of Mobile Users’ Activities Using 2 Mean-Normalization Method

Abstract

In recent decades, with the attracting features of mobiles including 4G and 5G, world is getting more connected to mobile communications. This results in the accumulation of large amount of data in the mobile network. The analysis of the network data is very complex but is essential in terms of resource and cost management. The network data analytics include detection of unusual network behaviour due to traffic created by the mobile users and Short Message Service (SMS) spammers. Research to an approach with the same impulsion is creating a new interest in the field of mobile network data analytics using machine learning tools. To attain this, Call Detail Record (CDR) provided by the telecom network industry is utilized. The timely analysis of CDR helps to understand the behaviour of the network due to various activities of mobile users. To analyse CDR, it has to be pre-processed to convert it from the raw data into machine understandable form. The proposed method is mean-normalization pre-processing which is suitable in understanding the behaviour of mobile users’ individual activities like incoming-outgoing calls, incoming-outgoing SMS and internet activity. Later, machine learning tools can be applied to analyse and predict the network anomalies like network traffic and Short.

Keywords

• call detail record
• machine learning tool
• mobile user activity
• network anomalies
• pre-processing