基于信息熵的商业银行客户画像属性约简研究

Attribute reduction of commercial bank customer portrait based on information entropy

客户画像是商业银行近年来的研究热点,从高维复杂的客户数据中筛选出有效属性是客户画像中的关键问题.为解决商业银行客户画像过程中,由于银行客户数据维度较高,难以实现精准画像的问题,在对客户数据进行聚类分析的基础上,结合粗糙集理论和信息熵理论,对商业银行投资客户画像属性进行了约简,并提出了属性约简算法,结果表明,该算法能够对银行客户画像数据进行有效约简,可以为属性筛选提供依据.

Customer portrait is a hot topic in commercial banks.One core issue of customer portrait is to select effective attributes from high-dimensional and complex customer data.The high-dimensional data makes it challenging to conduct customer portrait accurately.Based on the clustering analysis of customer data,we combine the rough set theory and information entropy to study the attribute reduction of customer portraits in commercial banks,and propose the attribute reduction algorithm.The experimental results show that the proposed method can obtain the optimal attribute reduction of customer portrait and can provide a basis for attribute selection.