Credit card customer analysis based on panel data clustering

被引：7
|
作者：
Nie, Guangli [1 ,2 ]
Chen, Yibing [2 ,3 ]
Zhang, Lingling [1 ,2 ]
Guo, Yuhong [3 ]
机构：
[1] Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China
[2] Grad Univ Chinese Acad Sci, Beijing 100190, Peoples R China
[3] Univ Int Relat, Beijing 100091, Peoples R China
来源：
ICCS 2010 - INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, PROCEEDINGS | 2010年 / 1卷 / 01期
基金：
中国国家自然科学基金; 北京市自然科学基金;
关键词：
panel data; data mining; clustering; credit card churn analysis; DISCOVERY;
D O I：
10.1016/j.procs.2010.04.281
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper, we propose a new distance measurement which can be used in panel data clustering. The distance as we designed can be calculated with weight and without weight. If users put more attention on recent data, a heavier weight can be assigned to the recent data. We use real panel data of a commercial bank's credit card to examine the performance of our new distance measurement. The results show that our distance measurement can reflect the information of different periods and panel data can be used to cluster to find new knowledge. This study discovers different knowledge structure from the traditional econometrics analysis with the help of data mining algorithms. (C) 2010 Published by Elsevier Ltd.
引用
收藏
页码：2483 / 2491
页数：9
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