Journal of Hebei University(Natural Science Edition) >

2022 , Vol. 42 >Issue 1: 98 - 104

DOI: https://doi.org/10.3969/j.issn.1000-1565.2022.01.014

Attribute reduction of commercial bank customer portrait based on information entropy

  • ZHANG Yujing ,
  • WANG Liu ,
  • QI Xiaona ,
  • XU Meiling ,
  • WANG Lei
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  • School of Computer and Information Engineering, Hebei Finance University, Baoding 071000, China

Received date: 2021-01-22

  Online published: 2022-02-22

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Abstract

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.

Key words： customer portrait; cluster; information entropy; attribute reduction

Cite this article

ZHANG Yujing , WANG Liu , QI Xiaona , XU Meiling , WANG Lei . Attribute reduction of commercial bank customer portrait based on information entropy[J]. Journal of Hebei University(Natural Science Edition), 2022 , 42(1) : 98 -104 . DOI: 10.3969/j.issn.1000-1565.2022.01.014

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