基于Apriori算法的食品抽检数据的关联规则挖掘

目的 为了发现检测数据的不合格项目之间有意义的关联规则, 并对挖掘出的关联规则进行分析解读, 进一步发掘了食品抽检数据的价值, 从而对食品安全监管具有一定的指导意义。方法 本文对利用Apriori算法对2015~2019年间山东食品药品监督管理局网站公布的食品安全抽检数据的不合格项目进行了关联规则挖掘。结果 通过挖掘得出最符合要求的10条规则。结论 利用关联规则挖掘算法对食品检验数据进行挖掘, 能够挖掘出有价值、有意义的规则, 对食品安全管理具有指导意义, 从中也可以看出数据挖掘技术在食品安全数据挖掘分析中具有广阔的应用前景。

Mining association rules of food sampling data based on Apriori algorithms

Objective To find the meaningful association rules among the unqualified items of the test data, and to analyze and interpret the association rules, further discover the value of the food sampling data, so as to have a certain guiding significance for the food safety supervision. Methods In this paper, the association rules of the unqualified items of the food safety sampling inspection data published on the website of Shandong food and drug administration from 2015 to 2019 were mined by using Apriori algorithm. Results Through the excavation, we got 10 rules that most meet the requirements. Conclusion Using association rules mining algorithm to mine the food inspection data, we can mine the valuable and meaningful rules, which has the guiding significance to the food safety management. From this, we can see that data mining technology in food safety data mining analysis has a broad application prospect.