Misdiagnosis Features of Ancient Clinical Records Based on Apriori Algorithm

Objective:To analyze misdiagnosis features in clinical cases of“Classified Medical Cases of Famous Physicians”and“Supplement to Classified Case Records of Celebrated Physicians.”Materials and Methods:Two hundred and five ancient misdiagnosed cases were analyzed in aspects of locations(exterior-interior type,qi-blood type and Zang‑Fu organs type)and patterns(heat-cold type and deficiency-excess type)by Apriori Algorithm Method.Results:The main types of misdiagnosis in those medical casesare as follows::Zang‑Fu location misjudgment,misjudging the interior as the exterior,misjudging deficiency pattern as excess pattern,and misjudging cold pattern as heat pattern.Among them,the most outstanding type is the misjudgment of deficiency–cold pattern as excess–heat pattern.Conclusions:(1)Accurate judgment of location and differentiation of deficiency and excess patterns are the key points in diagnosing the diseases correctly.The confusion of true deficiency–cold and pseudo‑excess–heat pattern should be taken seriously.(2)Data mining on ancient clinical cases offers a new methodology for assisting clinical diagnosis of traditional Chinese medicine.

Digital Twin-based Quality Management Method for the Assembly Process of Aerospace Products with the Grey-Markov Model and Apriori Algorithm

The assembly process of aerospace products such as satellites and rockets has the characteristics of single-or small-batch production,a long development period,high reliability,and frequent disturbances.How to predict and avoid quality abnormalities,quickly locate their causes,and improve product assembly quality and efficiency are urgent engineering issues.As the core technology to realize the integration of virtual and physical space,digital twin(DT)technology can make full use of the low cost,high efficiency,and predictable advantages of digital space to provide a feasible solution to such problems.Hence,a quality management method for the assembly process of aerospace products based on DT is proposed.Given that traditional quality control methods for the assembly process of aerospace products are mostly post-inspection,the Grey-Markov model and T-K control chart are used with a small sample of assembly quality data to predict the value of quality data and the status of an assembly system.The Apriori algorithm is applied to mine the strong association rules related to quality data anomalies and uncontrolled assembly systems so as to solve the issue that the causes of abnormal quality are complicated and difficult to trace.The implementation of the proposed approach is described,taking the collected centroid data of an aerospace product’s cabin,one of the key quality data in the assembly process of aerospace products,as an example.A DT-based quality management system for the assembly process of aerospace products is developed,which can effectively improve the efficiency of quality management for the assembly process of aerospace products and reduce quality abnormalities.

A Forensic Investigation of Terrorism in Nigeria: An Apriori Algorithm Approach

Investigations towards studying terrorist activities have recently attracted a great amount of research interest. In this paper, we investigate the use of the Apriori algorithm on the Global Terrorism Database (GTD) for forensic investigation purposes. Recently, the Apriori algorithm, which could be considered a forensic tool, has been used to study terrorist activities and patterns across the world. As such, our motivation is to utilise the Apriori algorithm approach on the GTD to study terrorist activities and the areas/states in Nigeria with high frequencies of terrorist activities. We observe that the most preferred method of terrorist attacks in Nigeria is through armed assault. Again, our experiment shows that attacks in Nigeria are mostly successful. Also, we observe from our investigations that most terrorists in Nigeria are not suicidal. The main application of this work can be used by forensic experts to assist law enforcement agencies in decision making when handling terrorist attacks in Nigeria.

Design and Implementation of Book Recommendation Management System Based on Improved Apriori Algorithm

The traditional Apriori applied in books management system causes slow system operation due to frequent scanning of database and excessive quantity of candidate item-sets, so an information recommendation book management system based on improved Apriori data mining algorithm is designed, in which the C/S (client/server) architecture and B/S (browser/server) architecture are integrated, so as to open the book information to library staff and borrowers. The related information data of the borrowers and books can be extracted from books lending database by the data preprocessing sub-module in the system function module. After the data is cleaned, converted and integrated, the association rule mining sub-module is used to mine the strong association rules with support degree greater than minimum support degree threshold and confidence coefficient greater than minimum confidence coefficient threshold according to the processed data and by means of the improved Apriori data mining algorithm to generate association rule database. The association matching is performed by the personalized recommendation sub-module according to the borrower and his selected books in the association rule database. The book information associated with the books read by borrower is recommended to him to realize personalized recommendation of the book information. The experimental results show that the system can effectively recommend book related information, and its CPU occupation rate is only 6.47% under the condition that 50 clients are running it at the same time. Anyway, it has good performance.

An Improved Apriori Algorithm

In this paper,We study the Apriori and FP-growth algorithm in mining association rules and give a method for computing all the frequent item-sets in a database.Its basic idea is giving a concept based on the boolean vector business product,which be computed between all the businesses,then we can get all the two frequent item-sets(minsup=2).We basis their inclusive relation to construct a set-tree of item-sets in database transaction,and then traverse path in it and get all the frequent item-sets.Therefore,we can get minimal frequent item sets between transactions and items in the database without scanning the database and iteratively computing in Apriori algorithm.

Application Analysis of Nursing Students'Grades in Course Relevance Based on Association Rule Mining Algorithm Apriori

By analyzing the correlation between courses in students’grades,we can provide a decision-making basis for the revision of courses and syllabi,rationally optimize courses,and further improve teaching effects.With the help of IBM SPSS Modeler data mining software,this paper uses Apriori algorithm for association rule mining to conduct an in-depth analysis of the grades of nursing students in Shandong College of Traditional Chinese Medicine,and to explore the correlation between professional basic courses and professional core courses.Lastly,according to the detailed analysis of the mining results,valuable curriculum information will be found from the actual teaching data.

谱聚类和Apriori算法在建筑坍塌事故致因组合分析中的应用

建筑坍塌事故是人员伤亡和经济损失较大的事故类型之一。为探究建筑坍塌事故不同致因之间的关联和相互依存关系,首先,选取国内2015—2020年231份建筑坍塌事故报告作为研究对象,借助R语言平台进行文本挖掘,得到43个致因。其次,运用Python进行谱聚类,根据致因之间的关联强度对其进行聚类。最后,利用关联规则挖掘Apriori算法确定建筑坍塌事故致因之间的关键关联组合。结果表明,43个事故致因可分为5类,在每一个簇类中确定了最关键的致因组合,并提出了针对性的预防措施,为坍塌事故的预防和控制提供一种新的思路。

基于Apriori的视觉传感网络关联数据高效识别方法

在视觉传感网络关联数据识别中,由于数据变化的规律难以捕捉,造成识别结果精确度降低,为此,提出了基于Apriori的视觉传感网络关联数据高效识别方法。通过设计视觉传感网络联合监控系统监控视觉传感网络数据变化情况,获取视觉传感网络关联数据。该系统由处理器单元、数据传输单元等四个模块构成。提取图像中的感兴趣区域完成ROI提取,并通过腐蚀处理等方式对视觉传感网络关联数据实施形态学处理。基于Apriori算法实施视觉传感网络关联数据之间关联规则的挖掘,实现视觉传感网络关联数据的识别。布设实验环境后,使用三个数据集测试该方法的识别性能。测试结果表明,所提方法的微平均查全率均高于94.86%,微平均查准率高于93.02%,最高召回率为98.52%,三个项目的测试值均较高,证明了该方法对视觉传感网络关联数据识别比较准确。

基于改进Apriori算法的学生网上学习行为关联特征挖掘模型

学生在线学习行为与成绩之间存在密切关联,为了挖掘学生学习行为特征间的隐含关系,研究采用改进的关联规则挖掘算法对其进行相关性分析。实验结果表明,研究设计的算法在精确率及召回率指标上表现较好,相比传统的关联规则挖掘算法运行速度更快,用时降低幅度达26.49%。该方法从多维度实现了学习行为的分析与关联,为教学决策和研究提供了科学的支持和指导。

基于Apriori算法的生鲜蔬菜购买的关联分析

为满足当下生鲜超市日常补货和定价需求,基于某商超经销的6个蔬菜品类从2020年7月1日至2023年6月30日的销售流水明细与批发价格的相关数据,采用Apriori算法和K-means++算法研究各生鲜蔬菜的销售时间分布规律及消费者购买各生鲜蔬菜的相关关系,进而从蔬菜种类销售购买关联度及相关性角度为生鲜超市的策略制定提供新的视角。

基于改进遗传算法和Apriori算法的气旋强度预测方法

为了提高气旋强度预测的准确度,在遗传算法和多支持度阈值的Apriori算法基础上提出了气旋预测算法GA-MSApriori。利用滑动窗口对历史气旋监测数据进行处理,经过处理后的数据能够描述各监测要素值所处范围以及要素值在过去时间内的变化情况,并以此作为输入数据,最终输出预测准确度较高的气旋强度预测规则。相较于传统的预测方法,气旋强度的预测准确度提高了6%~10%。

基于优化Apriori算法的印刷检测数据关联分析

为了解决印刷企业的印刷缺陷检测问题,采用优化Apriori关联分析挖掘方法,从而找出印刷缺陷检测数据之间的关联。首先,搭建印刷工厂管理与印刷数据采集系统;其次,优化Apriori算法的运行效率;最后,进行关联分析得出生产数据之间的关联规则。实验表明,该方法在印刷缺陷检测数据的分析中具有可行性,对降低印刷缺陷率有着重要的指导作用。

Apriori算法在“信息技术基础数学”考试成绩分析中的应用

文章将“信息技术基础数学”期末考试成绩作为研究对象,应用Apriori关联算法对学生考试成绩数据进行挖掘。实例验证显示,该算法能够发现试题中隐藏的知识点关联规则,有助于学生更好地构建自己的知识关联网络,并在下一轮教学活动中为教师提供重要的参考。

Novel Apriori-Based Multi-Label Learning Algorithm by Exploiting Coupled Label Relationship

It is a key challenge to exploit the label coupling relationship in multi-label classification（MLC）problems.Most previous work focused on label pairwise relations,in which generally only global statistical information is used to analyze the coupled label relationship.In this work,firstly Bayesian and hypothesis testing methods are applied to predict the label set size of testing samples within their k nearest neighbor samples,which combines global and local statistical information,and then apriori algorithm is used to mine the label coupling relationship among multiple labels rather than pairwise labels,which can exploit the label coupling relations more accurately and comprehensively.The experimental results on text,biology and audio datasets shown that,compared with the state-of-the-art algorithm,the proposed algorithm can obtain better performance on 5 common criteria.

基于APRIORI算法的安全本科模块化课程内容优化

为培养安全工程专业多方面的技术性实用人才,探究课程内容重复带来的问题以及对应的解决办法,统计分析某校安全工程专业28门课程教学大纲知识点的重复性,构建相关数据集,进而运用APRIORI算法分析课程的关联性数据,关联规则涵盖课程的所有重复知识点,之后基于计算结果给出可能原因及相应影响,同时与其他课程教授内容对比,分析此类问题的普遍性,并给出降低课程内容重复度的基本方法。结果表明:安全专业交叉性强,使得教材内容易出现重复,统计样本中共有41个知识点存在不同程度的重复;教学大纲知识点与课程之间共有10条单项集强关联规则,涵盖11门课程,支持度和置信度都在较高水平,显示出各课程内容之间的强相关现象;课程重复性高主要由教学设计不合理、课程管理制度不完善导致,可通过优化课程衔接、突出内容特色和采用多种教学方式等途径解决。

基于Apriori算法的智能配变终端设备缺陷自动诊断方法

为切实增强电力配变终端设备运行维护水平和管理控制程度,提出基于Apriori算法的智能配变终端设备缺陷自动化诊断方法。运用Apriori算法挖掘智能配变终端设备数据特征,组建三角分解树,采用粗糙集约简完成数据预处理,计算设备状态特征量,构建终端状态诊断证据体系,利用基础概率分配函数方法减少缺陷样本训练偏差,将缺陷相关系数替换为加权平均模糊隶属度,创建智能配变终端设备的优良、一般与预警隶属度函数,通过D-S证据理论完成缺陷数据融合,自动化诊断智能配变终端设备缺陷。仿真实验表明方法具备优秀的诊断精度与收敛速率。

基于Apriori算法的关联规则分析应用

Apriori算法是关联规则的经典算法之一。文中是基于对Apriori算法原理的研究从而完成电影标签推荐的应用,实现当用户给自己喜欢的电影打标签时,可通过关联规则进行电影推荐。实验采用Python语言实现Apriori算法,对数据集MovieLens采用One-hot编码进行处理,利用Apriori算法寻找到数据集中的频繁项集,根据找到的频繁项集生成关联规则。实验结果表明,基于Apriori算法的电影标签推荐得到的实验结果更准确、快速、高效,同时也取得了良好的电影标签推荐效果,从而提升用户体验。

基于Apriori算法的大学生体测项目关联规则挖掘

大学生身体素质是力量、速度、耐力、柔韧和灵敏的综合表现,为了测量大学生的身体素质情况并分析改善大学生的身体素质,本文分别测量了大学生中男生的各项指标(引体向上、50 m、1000 m、坐位体前屈和跳远)和女生的各项指标(一分钟仰卧起坐、50 m、800 m、坐位体前屈和跳远)。本文使用Apriori算法分别做了3组实验,即在最小支持度为50%和最小置信度为70%,最小支持度为60%和最小置信度为70%,最小支持度为70%和最小置信度为70%的前提下对某大学近5年的男生和女生的各项指标进行关联规则挖掘。实验结果表明:体重正常的学生每项体测分别及格的最小置信度都为70%以上,在所有关联项结果中最高的最小置信度为肺活量及格与体重正常的最小置信度为87.7%,而体重异常的学生体测分别及格的最小置信度都不大于70%。在所有关联项结果中身高与各项体测之间的最小置信度差异不明显。这验证了Apriori算法在大学生身体素质关联规则挖掘中发挥着重要的作用,利用挖掘出来的频繁项集,能够很好地辅助各大高校改善大学生的身体素质。

改进Apriori算法的高校图书馆图书智能推荐系统

为了提高高校图书馆图书推荐的准确性,设计了基于改进Apriori算法的高校图书馆图书智能推荐系统。首先描述高校图书馆图书智能推荐系统的基本架构,并通收集高校图书智能推荐系统中的借阅者和图书相关信息,然后引入Apriori算法对借阅者和图书之间的关联进行分析和挖掘,建立两者之间的关联规则,并针对Apriori算法的不足进行相应的改进,最后根据建立的关联规则进行借阅者的个性化图书信息推荐,采用具体仿真实验分析了与其它高校图书馆图书智能推荐系统的性能。结果表明,改进Apriori算法的高校图书馆图书推荐精度超过95%,推荐时间控制在有效范围内,提高了高校图书馆图书智能推荐效率,获得了比较其它高校图书馆图书智能推荐系统更加理想的结果。