Change mining of customer profiles based on transactional data
This data was imported from DBLP:
Authors: Apeh, E.T. and Gabrys, B.
Editors: Spiliopoulou, M., Wang, H., Cook, D.J., Pei, J., Wang, W., Zaïane, O.R. and Wu, X.
Journal: ICDM Workshops
Publisher: IEEE Computer Society
This data was imported from Scopus:
Authors: Apeh, E. and Gabrys, B.
Journal: Proceedings - IEEE International Conference on Data Mining, ICDM
Customer transactions tend to change overtime with changing customer behaviour patterns. Classifier models, however, are often designed to perform prediction on data which is assumed to be static. These classifier models thus deteriorate in performance overtime when predicting in the context of evolving data. Robust adaptive classification models are therefore needed to detect and adjust to the kind of changes that are common in transactional data. This paper presents an investigation into using change mining to monitor the adaptive classification of customers based on their transactions through a moving time window. Results from our experiments show that our approach can be used for learning and adapting to changing customer profiles. © 2011 IEEE.