Abstract:
Credit card classification is a system
for credit card users which is used to assign either
a "good credit card ",which is likely to repay
financial obligation, or a "bad credit card ",
which has high possibility of defaulting on
financial obligation. In a credit card
classification, a credit card user’s data isusually
assessed and evaluated, like his financial status,
annual and monthly income, assets and liabilities
and previous past payments to distinguish between
a “good” and a “bad” credit card for the
user.This paper presents the automatic credit card
classification using integration of clustering and
classification algorithm. The goal of this paperisto
predict the status of credit card such as good or
bad. The empirical study between the integration
of hierarchical agglomerative algorithm and C 4.5
decision tree algorithm and traditional C4.5
decision tree algorithm areapplied based on
Stalog (“German credit data”) dataset from UCI
machine learning repository. Then, the accuracies
of these two algorithms are compared. According
to experimental results, the integration of
hierarchical agglomerative clustering and C4.5
decision tree could achieve higher accuracy than
the traditional C4.5 decision tree.
