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Supply chain product optimal configuration based on data mining

  • Hong Zhen Zheng*
  • , Dian Hui Chu
  • , Chun Jiao Xu
  • *Corresponding author for this work
  • School of Computer Science and Technology (School of Software), Harbin Institute of Technology Weihai

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

Abstract

This paper takes customer preference sequence data and proposes a relationship model between customer preference orientation and customer characteristics which use the characteristics of the supply chain to draw on symbolic sequence clustering. It focuses on the study of symbol sequence data properties, and analyzes the essence of preference symbol sequence clustering based on symbolic sequencing in both formalized and materialized ways. It studies the application of the self-organizing feature map as a symbol sequence clustering algorithm, and makes the comparison between clustering models so that the.

Original languageEnglish
Title of host publicationFuzzy System and Data Mining - Proceedings of FSDM 2015
EditorsGang Chen, Feng Liu, Mohammad Shojafar
PublisherIOS Press BV
Pages363-368
Number of pages6
ISBN (Electronic)9781614996187
DOIs
StatePublished - 2016
Externally publishedYes
Event2015 International Conference on Fuzzy System and Data Mining, FSDM 2015 - Shanghai, China
Duration: 12 Dec 201515 Dec 2015

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume281
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

Conference

Conference2015 International Conference on Fuzzy System and Data Mining, FSDM 2015
Country/TerritoryChina
CityShanghai
Period12/12/1515/12/15

Keywords

  • Data mining
  • Optimal allocation
  • Symbol sequence clustering

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