A sequential model of R&D investment over an unbounded time horizon
Management Science
Algorithms for clustering data
Algorithms for clustering data
The String-to-String Correction Problem
Journal of the ACM (JACM)
A clustering algorithm for asymmetrically related data with applications to text mining
Proceedings of the tenth international conference on Information and knowledge management
Clustering Algorithms
Combination of multiple classifiers for the customer's purchase behavior prediction
Decision Support Systems - Special issue: Agents and e-commerce business models
Customer-adapted coupon targeting using feature selection
Expert Systems with Applications: An International Journal
New Frontiers in Applied Data Mining
Journal of Intelligent Information Systems
Exploiting randomness for feature selection in multinomial logit: a CRM cross-sell application
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Contextual trace-based video recommendations
Proceedings of the 21st international conference companion on World Wide Web
Modeling partial customer churn: On the value of first product-category purchase sequences
Expert Systems with Applications: An International Journal
Fuzzy clustering of human activity patterns
Fuzzy Sets and Systems
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The inability to capture sequential patterns is a typical drawback of predictive classification methods. This caveat might be overcome by modeling sequential independent variables by sequence-analysis methods. Combining classification methods with sequence-analysis methods enables classification models to incorporate non-time varying as well as sequential independent variables. In this paper, we precede a classification model by an element/position-sensitive Sequence-Alignment Method (SAM) followed by the asymmetric, disjoint Taylor-Butina clustering algorithm with the aim to distinguish clusters with respect to the sequential dimension. We illustrate this procedure on a customer-attrition model as a decision-support system for customer retention of an International Financial-Services Provider (IFSP). The binary customer-churn classification model following the new approach significantly outperforms an attrition model which incorporates the sequential information directly into the classification method.