A Data Mining Approach to Classify Credit Cardholders? Behavior
Aihua Li, Graduate University of Chinese Academy of Sciences
Yong Shi, Graduate University of Chinese Academy of Sciences
Meihong Zhu, Capital University of Economics and Business, Beijing, China
Jingran Dai, Rensselaer Polytechnic Institute, Troy, New York USA
Credit cardholders? behavior analysis is an important issue to be studied. Multi-criteria linear programming (MCLP) classification method has shown its advantage in fast speed and balanced classification accuracy on this problem. However, dimension reduction is necessary before some classification methods implementing, not only for faster classification speed but also for commercial knowledge discovering. In this paper, a data mining approach based on the combination of MCLP and Principal Component Analysis (PCA) is proposed, and the influence of PCA on MCLP classification method is studied. One dataset, which comes from a bank in US, is used to test the performance of this approach, and the advantage of this classification method is shown by experiments.
Citation:
Aihua Li, Yong Shi, Meihong Zhu, Jingran Dai, "A Data Mining Approach to Classify Credit Cardholders? Behavior," icdmw,pp.828-832, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06), 2006