摘要
本文从优化初始簇入手,提出了改进的聚类算法,提高了信贷风险识别效率及准确率.主要工作包括:(1)实现基于信贷特色的申贷数据集标准化算法;(2)提出δ-相似度度量概念;(3)提出基于δ-K means的信贷风险识别算法δ-KCLR(δ-K-means-risk analysis of thebank credit)算法;(4)实验表明在银行信贷业务分析中,采用δ-KCLR算法可以有效识别隐含在信贷业务中的信贷风险.用这一模型可指导或预测新增贷款人中是否存在贷款风险.
The paper focus on the optimization initial clusters, proposed a novel modified clustering method,improved the identification efficiency and accuracy. The main contributions include. （1） implementing loan data standardization algorithm based on commercial loan features. （2） proposing a novel concept δ-similarity. （3）proposing loan risk identification algorithm based on δ-K means. （4） By experiments showing that δ-KCLR （δ-K-means-risk analysis of the bank credit） algorithm can effectively identify the risks in commercial loans.
出处
《四川大学学报（自然科学版）》
CAS
CSCD
北大核心
2009年第4期924-928,共5页
Journal of Sichuan University(Natural Science Edition)
基金
国家自然科学基金(60773169)
关键词
信贷风险
聚类
K均值
δ-簇
commercial loan, clustering, K-means, δ-Cluster
