摘要
为了能够及时、恰当地进行个人信用评估分析,加快信用卡发卡机构的决策速度,介绍了数据挖掘技术在信用卡公司对用户评估中的应用,对比分析了数理统计模型、分类-聚类个人信用评估模型等几种个人信用评估模型建模方法的优缺点。建立了一种决策树-神经网络个人信用评估模型,针对该模型提出了一种近邻聚类算法。该算法不需要事先给定聚类的类别数,可以进行无监督学习。通过对比分析可知,该算法在个人信用评估应用中可以得到较理想的结果。
For the purpose of process the personal credit evaluating timely and correctly,increase the decision rate,this paper describes the requirement of the credit card company for data mining and neural network technology which apply for personal credit evaluating. Contrasted and analyzed some of personal credit evaluating model, e. g. statistical model, elassification- cluatering model, and so on. Demonstrated those excellence and disadvantage. Constructed a decision tree -neural network personal credit evaluating model. At last,give a vicinage- extended clustering algorithm,the algorithm needn＇t give number of clustering, and can put up unsupervised learning. The algorithm is more fit for personal credit evaluating than other methods.
出处
《计算机技术与发展》
2006年第12期172-174,177,共4页
Computer Technology and Development
基金
河南省自然科学基金(0511011500)
关键词
信用评估
分类
聚类
决策树
credit evaluating
classification
clustering
decision tree
