摘要
食品安全是社会各界日益关注的民生问题,政府部门正在逐步完善监管体制、加大监管力度,构建社会共治的格局。本文针对已经曝光的食品安全事件,经过清洗筛选建立统一规范的数据存储,利用改进的基于信息熵模糊聚类分析算法对其进行数据挖掘,以便发现这些事件中具有象征性的现象以及典型性的安全事件,从而为政府制定管理决策和为民众提高防范意识提供参考性依据。实验中将改进的算法运行在UCI数据集上验证算法的有效性,结果表明该算法进一步提高了聚类的正确率、类精度及召回率。
Food safety is an increasingly concerned issue of people’s livelihood. At present,the national government is gradually improving the regulatory system and increasing regulatory efforts,and building the pattern of joint governance. In view of the food safety events that have been exposed,a unified and standardized data storage is established after screening and cleaning,and the improved clustering algorithm based on information entropy is used to analyze and study them,so as to find the rules in these events and the typical safety problems which can provide a reference for the government to make management decisions and improve the public awareness of prevention. In the experiment,the improved fuzzy K-Modes clustering algorithm is run on the UCI data set to test the effectiveness of the algorithm. The results show that the improved algorithm further improves the clustering quality.
作者
辜萍萍
GU Pingping(Computer Science Department,Tan Kah Kee College,Zhangzhou Fujian 363105,China)
出处
《智能计算机与应用》
2021年第5期188-192,共5页
Intelligent Computer and Applications
关键词
食品安全
数据挖掘
聚类分析
信息熵
food safety
data mining
cluster analysis
information entropy
