Multi-dimensional model-based clustering for user-behavior mining in telecommunications industry

We develop an innovative sequential data mining system for mining the customers´ churning behaviors for the telecommunications industry. Recently, an increasing number of telecommunications customers are switching from one service or service provider to another. This phenomenon is called ´churn´, which is a major cause of corporations´ loss of profitability. It is important for a telecommunications company to find out the transitional behavior of its customers through data mining. Our approach is to use a model-based clustering method, extended to handle multi-dimensional data, to automatically and efficiently partition the customer behavior according to their behavior. We model this problem as a sequential clustering problem, and present an effective solution for solving the problem when the elements in the sequences are of a multi-dimensional nature. We provide theory and algorithms for the task, and empirically demonstrate that the method is effective in mining the customers data for the telecommunications industry.