基于改进FP-growth算法的食品风险因素关联分析方法  

Association Analysis of Food Risk Factors Based on Improved FP-growth Algorithm

作　　者：于家斌[1,2] 马欣玥 赵峙尧 王小艺 张新 崔晓玉 白玉廷[1,2] 陈帅祥 YU Jiabin;MA Xinyue;ZHAO Zhiyao;WANG Xiaoyi;ZHANG Xin;CUI Xiaoyu;BAI Yuting;CHEN Shuaixiang(School of Computer and Artificial Intelligence,Beijing Technology and Business University,Beijing 100048,China;Key Laboratory of Industrial Internet and Big Data,China National Light Industry,Beijing Technology and Business University,Beijing 100048,China;School of Arts and Sciences,Beijing Institute of Fashion Technology,Beijing 100029,China)

机构地区：[1]北京工商大学计算机与人工智能学院,北京100048 [2]北京工商大学中国轻工业工业互联网与大数据重点实验室,北京100048 [3]北京服装学院文理学院,北京100029

出　　处：《食品科学》2024年第23期250-258,共9页Food Science

基　　金：国家重点研发计划项目(2022YFF1101103);北京市自然科学基金项目(4222042,6242004);北京市科技新星计划项目(20240484720);北京市属高校优秀青年人才培育计划项目(BPHR202203043)。

摘　　要：为解决传统食品安全监督抽检“随机抽”模式存在的抽检决策主观性强、靶向性不高的问题,本研究提出一种基于改进Frequent Pattern-growth(FP-growth)算法的食品风险因素关联分析方法。首先,采用熵权法分别对食品种类的风险指标进行权重分配,以计算出不同食品种类的风险指数。其次,以风险指数为特征,基于小批量K均值算法(MiniBatchKmeans)进行风险聚类,得到食品的风险等级。最后,采用带约束的改进FP-growth算法进行食品风险因素关联规则挖掘,挖掘食品风险等级与食品种类、时间、地域属性信息之间的关联关系,并对挖掘出的结果进行关联分析,从而为精准靶向引导抽检决策提供指导。本研究依托2019年中国某些地区的食品抽检数据进行分析,对其进行指标赋权,计算风险指数;后经过风险聚类为低风险、中风险和高风险;最后,将数据导入改进FPgrowth算法,得到食品风险因素关联规则。通过对比实验得到结果:对于17214条抽检数据,本研究提出的改进FP-growth算法相较于Apriori算法运行时间短;相较于传统FP-growth算法,删除了无效规则,提高了对食品风险因素关联规则的分析效率,从而为食品监管部门抽检工作提供了准确、高效的决策依据。In order to solve the problems of strong subjectivity and low targeting in sampling decision-making that exist in food safety surveillance sampling,this study proposed a correlation analysis method based on an improved Frequent Pattern-growth(FP-growth)algorithm for food risk factors.First,the entropy weight method was used to assign weights to the risk indicators of food categories so as to calculate the risk indices of different food categories.Second,the risk index was used as a feature for risk clustering based on MiniBatchKmeans to obtain the risk level of food products.Finally,an improved FP-growth algorithm with constraints was used for association rule mining of food risk factors to excavate the association relationship between the risk level of food products and the information of food types,time,and geographic attributes,and the mined results were analyzed by correlation analysis so as to provide guidance for precise targeting to guide the decision making of sampling inspection.This study was based on food sampling data from certain regions of China in 2019,which were assigned with indicators to calculate the risk index.Afterwards,the risk was clustered into low(L),medium(M),and high risk(H).Finally,the data was imported into the improved FP-growth algorithm to obtain the association rules of food risk factors.For 17214 pieces of sampling data,the improved FP-growth algorithm had a shorter running time when compared with the Apriori algorithm.Compared with the traditional one,the improved FP-growth algorithm removed invalid rules and improved the analysis efficiency of the association rules of food risk factors.Thus,it provides an accurate and efficient decision-making basis for the sampling work of food regulatory authorities.

关 键 词：食品安全监督抽检 关联分析 熵权法 MinibatchKmeans聚类 Frequent Pattern-growth算法 

分 类 号：TS210.1[轻工技术与工程—粮食、油脂及植物蛋白工程] TP183[轻工技术与工程—食品科学与工程]