Credit card customer analysis based on panel data clustering

Abstract

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.