DATA MINING VIA MULTIPLE CRITERIA LINEAR PROGRAMMING: APPLICATIONS IN CREDIT CARD PORTFOLIO MANAGEMENT

Yong Shi (), Yi Peng (), Weixuan Xu () and Xiaowo Tang ()
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Yong Shi: College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA
Yi Peng: College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA
Weixuan Xu: Institute of Policy and Management, Chinese Academy of Sciences, Beijing, 100080, China
Xiaowo Tang: College of Management, Chinese University of Electronic Science and Technology, Chengdu, Sichuan 610054, China

International Journal of Information Technology & Decision Making (IJITDM), 2002, vol. 01, issue 01, 131-151

Abstract: Data mining becomes a cutting-edge information technology tool in today's competitive business world. It helps the company discover previously unknown, valid, and actionable information from various and large databases for crucial business decisions. This paper provides a promising approach of data mining to classify the credit cardholders' behavior through multiple criteria linear programming. After reviewing the history of linear discriminant analyses, we will describe first a model for classifying two-group (e.g. bad or good) credit cardholder behaviors, and then a three-group (e.g. bad, normal, or good) credit model. Besides the discussion of the modeling structure, we will utilize the well-known commercial software package SAS to implement this technology by using a real-life credit card data warehouse. A number of potential business and financial applications will be finally summarized.

Keywords: Data mining; classification; multiple criteria linear programming; credit cardholders' behavior (search for similar items in EconPapers)
Date: 2002
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Citations: View citations in EconPapers (7)

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DOI: 10.1142/S0219622002000038

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