Study Of Mobile User Stability Based On Data Mining

Nowadays the potential of undeveloped market becomes smaller, and major operatorsincreasingly focus on the development of stock market. Accompanying increasingcompetition among the communication industry, maintaining and improving the stabilityand loyalty of customers has become the key determinant of profitability. With massivebusiness data, the focus of operators is how to use existing data to predict future eventsand then provide real valuable knowledge for business decisions. Data mining techniqueshas become the effective measure of achieving this goal. In order to prevent the loss ofcustomers, we need to identify the stable users by using data mining model.Based on the data mining theory, this thesis introduces the application of data miningtechnology during the communication industry. The SEMMA methodology and Rsoftware are used to build data mining models. We can recognize a batch of stable mobileusers based on the sampling, cleaning, modeling, evaluation, prediction of the largebusiness data. With the model evaluation, we find that the Random Forest model performsthe best. Experiments also show that Random Forest model can describe and predict mostof the stable users in a shorter period of time, which provides the operator strategies foradopting reasonable precision marketing timely.