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  • 标题:Association analysis in food sampling inspection data
  • 本地全文:下载
  • 作者:Tongqiang Jiang ; Xin Chen ; Huan Jiang
  • 期刊名称:MATEC Web of Conferences
  • 电子版ISSN:2261-236X
  • 出版年度:2022
  • 卷号:355
  • 页码:1-5
  • DOI:10.1051/matecconf/202235502033
  • 语种:English
  • 出版社:EDP Sciences
  • 摘要:At present, China exists a problem that the cost of food sampling inspection is too high. This paper attempts to reduce the number of sampling inspection items in the same food category, reduce the cost of food sampling inspection, and improve the work efficiency through the association analysis of national sampling inspection data. And this paper applies Apriori algorithm to analyse the association rules, which is based on the unqualified pastry sampling inspection data in the 2019 national food sampling inspection database. Finally, we obtain 10 strong association rules through experiments. The results show that this association analysis can reduce the workload of food sampling inspection effectively.
  • 关键词:Apriori algorithm;Food sampling inspection data;Data mining
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