MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Visualization of navigation patterns on a Web site using model-based clustering
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Data Mining of User Navigation Patterns
WEBKDD '99 Revised Papers from the International Workshop on Web Usage Analysis and User Profiling
Mining Customer Value: From Association Rules to Direct Marketing
Data Mining and Knowledge Discovery
AUC: a statistically consistent and more discriminating measure than accuracy
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
A probabilistic approach to navigation in Hypertext
Information Sciences: an International Journal
Classifying execution times in parallel computing systems: a classical hypothesis testing approach
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Nearest-neighbor-based approach to time-series classification
Decision Support Systems
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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.