| 改进的关联规则在食品安全预警上的应用 |
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| 引用本文： | 肖克晶,左敏,王星云,刘婷.改进的关联规则在食品安全预警上的应用[J].北京工商大学学报(自然科学版),2017,35(2):89-94. |
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| 作者姓名： | 肖克晶 左敏 王星云 刘婷 |
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| 作者单位： | 北京工商大学 计算机与信息工程学院, 北京 100048,北京工商大学 计算机与信息工程学院, 北京 100048,北京工商大学 计算机与信息工程学院, 北京 100048,中国食品药品检定研究院, 北京 100050 |
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| 基金项目： | “十二五”国家科技支撑计划项目(2015BAK36B04)。 |
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| 摘 要： | 为将海量的食品检测数据有效地应用于食品安全预警,首先分析了食品检测数据的特点,以及传统的Apriori算法在挖掘食品检测数据上的不足,进而提出过滤算法,并将其作为Apriori算法的前置组件对算法进行改进,然后建立了食品安全预警模型,最后将实际的食用油检测数据用改进后的算法进行挖掘,发现其存在的潜在安全隐患进而做出风险预警。通过实验对比Apriori算法,发现改进后的算法摒弃了大量的伪关联规则,能有效提高食品安全预警的效率和准确度,具有十分重要的实际意义。
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| 关 键 词： | 关联规则 频繁项集 稀疏数据 过滤算法 |
| 收稿时间： | 2015/12/3 0:00:00 |
Application of Improved Association Rules on Food Safety Early Warning |
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| XIAO Kejing,ZUO Min,WANG Xingyun and LIU Ting.Application of Improved Association Rules on Food Safety Early Warning[J].Journal of Beijing Technology and Business University:Natural Science Edition,2017,35(2):89-94. |
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| Authors: | XIAO Kejing ZUO Min WANG Xingyun and LIU Ting |
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| Institution: | School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China,School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China,School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China and National Institutes for Food and Drug Control, Beijing 100050, China |
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| Abstract: | In order to the effective application of the massive detection data in food safety early warning, this paper analyzed the characteristics of the food detection data, and the insufficient of traditional Apriori algorithm on food detection data, then proposed the filtering algorithm, which is a pre-components of Apriori algorithm. An early warning model was established, which was applied to excavate the real oil detection data, and the potential safety problems were founded to make an early warning. Compared with the Apriori algorithm, the improved algorithm abandoned a lot of pseudo-association rules, and also could effectively enhance the efficiency and accuracy of food safety early warning, which has a very important practical significance. |
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| Keywords: | association rules frequent item sets sparse data filtering algorithm |
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