Preprocessing time series data for classification with application to CRM

Publication Type:
Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2005, 3809 LNAI pp. 133 - 142
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We develop an innovative data preprocessing algorithm for classifying customers using unbalanced time series data. This problem is directly motivated by an application whose aim is to uncover the customers' churning behavior in the telecommunication industry. We model this problem as a sequential classification problem, and present an effective solution for solving the challenging problem, where the elements in the sequences are of a multi-dimensional nature, the sequences are uneven in length and classes of the data are highly unbalanced. Our solution is to integrate model based clustering and develop an innovative data preprocessing algorithm for the time series data. In this paper, we provide the theory and algorithms for the task, and empirically demonstrate that the method is effective in determining the customer class for CRM applications in the telecommunications industry. © Springer-Verlag Berlin Heidelberg 2005.
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