Credit evaluation of gas consumers by combining hierarchy analysis with clustering

  • Wenqing Xu
  • , Huiqun Yu*
  • , Jianmei Guo
  • , Jinglei Shen
  • , Huaiying Sun
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Credit evaluation of customer is an important task for gas companies to achieve marketing management, and is of great significance to improve the economic efficiency of enterprises. By analyzing various factors influencing gas customer credit, a gas customer credit index hierarchy is established. Then, based on the credit index hierarchy, this paper proposes a hybrid credit evaluation method by cluster analysis and analytic hierarchy process(AHP). This method first divides gas customers into different groups by cluster analysis, then determines the credit index weight by AHP, and finally evaluates gas customer credit rating by combining the above two results. The empirical analysis of the actual data of a gas company shows that as a synthesis of customer data's statistical properties and gas professionals' actual work experience, this model can evaluate gas customer credit rating rationally and effectively.

Original languageEnglish
Title of host publicationProceedings - 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017
EditorsXiaohui Cui, Shaowen Yao, Simon Xu, Guobin Zhu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages827-832
Number of pages6
ISBN (Electronic)9781509055074
DOIs
StatePublished - 27 Jun 2017
Externally publishedYes
Event16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017 - Wuhan, China
Duration: 24 May 201726 May 2017

Publication series

NameProceedings - 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017

Conference

Conference16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017
Country/TerritoryChina
CityWuhan
Period24/05/1726/05/17

Keywords

  • Analytic hierarchy process
  • Cluster analysis
  • Credit evaluation
  • Gas consumer
  • Hybrid analysis
  • K-means algorithm

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