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.