A related degree-based frequent pattern mining algorithm for railway fault data

It is of great significance to improve the efficiency of railway production and operation by realizing the fault knowledge association through the efficient data mining algorithm.However,high utility quantitative frequent pattern mining algorithms in the field of data mining still suffer from the problems of low time-memory performance and are not easy to scale up.In the context of such needs,we propose a related degree-based frequent pattern mining algorithm,named Related High Utility Quantitative Item set Mining(RHUQI-Miner),to enable the effective mining of railway fault data.The algorithm constructs the item-related degree structure of fault data and gives a pruning optimization strategy to find frequent patterns with higher related degrees,reducing redundancy and invalid frequent patterns.Subsequently,it uses the fixed pattern length strategy to modify the utility information of the item in the mining process so that the algorithm can control the length of the output frequent pattern according to the actual data situation and further improve the performance and practicability of the algorithm.The experimental results on the real fault dataset show that RHUQI-Miner can effectively reduce the time and memory consumption in the mining process,thus providing data support for differentiated and precise maintenance strategies.

A Fast Distributed Algorithm for Association Rule Mining Based on Binary Coding Mapping Relation

Association rule mining is an important issue in data mining. The paper proposed an binary system based method to generate candidate frequent itemsets and corresponding supporting counts efficiently, which needs only some operations such as ＂and＂, ＂or＂ and ＂xor＂. Applying this idea in the existed distributed association rule mining al gorithm FDM, the improved algorithm BFDM is proposed. The theoretical analysis and experiment testify that BFDM is effective and efficient.

Residential Electricity Consumption Behavior Mining Based on System Cluster and Grey Relational Degree

In order to improve the utilization of the residential electricity consumption data which contains the information on the user’s electricity consumption habits, a residential electricity consumption behaviors mining algorithm model is constructed. Firstly, according to the attribute, the collected data can be divided into the global data and the phase data, then the appropriate global variables are selected to mine the user’s electricity consumption patterns in the near future on the system clustering algorithm. Based on the theory of grey relational analysis, combing phase data with the power modes to analyze the potential characteristics of residential electricity consumption behaviors deeply that verify the ability of latest power mode to predict household electricity consumption situation in the coming few days and the effect of dominant phase variables on the peak load shifting. Finally, from the actual data of a certain family, the proposed data mining algorithm is testified that it can effectively explore the electricity consumption behavior habits and characteristics of the family.

Selection method of risk response schemes for mining project based on fuzzy preference relations

To overcome the subjectivity of experts in the process of risk response scheme selection, according to the theory of group decision making, a selection method and flow of the risk response schemes for a mining project was proposed based on fuzzy preference relation and consistency induced ordered weighted averaging (C-IOWA) operator,which can overcome the loss of information in the process of group decision making to a great degree, and improve its efficiency and quality.A numeric example was introduced to illustrate the application of the method, also validating the method as scientific and practicable.

Study on coal mines accidents based on the grey relational analysis

The subject investigated the system of people-machine-environment in coal mines.The coal mines working process was researched and the theory of grey system was applied to analyze coal mines safety accidents and those relevant factors.This re- search reveals that this analysis method is easy and highly available and the result is of great credibility,which can not only provide theoretical supports to the quantitative study of coal mines safety accident,but offer basis for coal mines companies' safety management.

AN EVALUATION APPROACH FOR THE PROGRAM OF ASSOCIATION RULES ALGORITHM BASED ON METAMORPHIC RELATIONS

As data mining more and more popular applied in computer system,the quality as-surance test of its software would be get more and more attention.However,because of the ex-istence of the 'oracle' problem,the traditional test method is not ease fit for the application program in the field of the data mining.In this paper,based on metamorphic testing,a software testing method is proposed in the field of the data mining,makes an association rules algorithm as the specific case,and constructs the metamorphic relation on the algorithm.Experiences show that the method can achieve the testing target and is feasible to apply to other domain.

Effective Diagnosis of Lung Cancer via Various Data-Mining Techniques

One of the leading cancers for both genders worldwide is lung cancer.The occurrence of lung cancer has fully augmented since the early 19th century.In this manuscript,we have discussed various data mining techniques that have been employed for cancer diagnosis.Exposure to air pollution has been related to various adverse health effects.This work is subject to analysis of various air pollutants and associated health hazards and intends to evaluate the impact of air pollution caused by lung cancer.We have introduced data mining in lung cancer to air pollution,and our approach includes preprocessing,data mining,testing and evaluation,and knowledge discovery.Initially,we will eradicate the noise and irrelevant data,and following that,we will join the multiple informed sources into a common source.From that source,we will designate the information relevant to our investigation to be regained from that assortment.Following that,we will convert the designated data into a suitable mining process.The patterns are abstracted by utilizing a relational suggestion rule mining process.These patterns have revealed information,and this information is categorized with the help of an Auto Associative Neural Network classification method(AANN).The proposed method is compared with the existing method in various factors.In conclusion,the projected Auto associative neural network and relational suggestion rule mining methods accomplish a high accuracy status.

一种基于关联程度的高效用数量比频繁模式挖掘算法

高效用频繁模式挖掘算法运用数据项的重要度信息,能够从数据中挖掘出更重要的频繁模式,而高效用数量比频繁模式挖掘算法可以进一步研究频繁模式中数据项的数量比例关系,是目前数据挖掘领域中的研究课题。从提高算法性能和实用性的角度出发对高效用数量比频繁模式挖掘算法进行优化,提出了一种基于关联程度的高效用数量比频繁模式挖掘算法RHUQI-Miner。RHUQI-Miner首先提出关联程度的概念,依据关联程度构建项目关联程度结构,并给出关联剪枝优化策略,寻找关联程度更高的项目集合,减少冗余和无效的频繁模式;随后运用修正模式长度策略,修正挖掘过程中项集的效用信息,使算法可根据实际数据情况控制输出频繁模式的长度,进一步提升算法的性能,提高算法的实用性。通过对RHUQI-Miner在动车组PHM系统车载故障数据集上的实验结果进行分析,表明该算法能够有效减少挖掘过程中的时间以及内存消耗,可以得出该算法适用于铁路实际数据和业务的有效结论。

局部-全局特征引导的图文多级关系分析与挖掘方法

具有语义相关性的文本、图像数据往往具有互补性,可以从不同角度增强语义理解,因此,图文语义关系挖掘是图文数据得以充分利用的关键。为解决图文数据深层语义关系挖掘不充分、检索阶段预测不精准的问题,本文提出了一种局部-全局特征引导的多级关系分析与挖掘方法。采用多头自注意力机制的Transformer建模图像关系,构建图像引导的文本注意力模块,挖掘图像区域和全局文本间的细粒度关系,融合局部-全局特征有效增强图文数据的语义关系。为验证本文方法,在Flickr30K、MSCOCO-1K和MSCOCO-3K数据集上进行实验,并与VSM、SGRAF等13种方法进行对比分析,本文方法中以文索图的召回率平均提升了0.62%,以图索文的召回率平均提高了0.5%,实验结果验证了本文方法的有效性。

基于序关系分析—独立性权系数法的含水层富水性研究

为评价冯家塔煤矿4煤顶板山西组砂岩含水层富水性,选取了山西组富水砂岩厚度、岩心采取率、砂泥岩互层层数、脆塑性岩厚度比、砂岩岩性系数5个影响因素,利用序关系分析法求出影响山西组砂岩含水层富水性的主观权重,利用独立性权系数法求出客观权重,采用乘法合成法得出综合权重,构建了富水性评价模型。结果表明,冯家塔4煤顶板山西组砂岩含水层富水性较强和强富水区域主要分布在井田南部和西部,弱富水区域主要在井田北部及中部小部分区域。通过对比矿井物探、井下积水区及抽水试验的结果,预测模型准确率较高,与实际情况相符。

图文数据的多级关系分析与挖掘方法

如何高效挖掘多模态数据间隐藏的语义关联是当前多模态知识抽取的重点任务之一,为更细粒度地挖掘图像与文本数据间关系,提出了一种多级关系分析与挖掘(MRAM)方法,引入BERT-Large模型,提取文本特征构建文本连接图,利用Faster-RCNN网络提取图像特征来学习空间位置关系和语义关系并构建图像连接图,进而完成单模态内部语义关系计算,在此基础上,使用节点切分方法和带多头注意力机制的图卷积网络(GCN-MA)进行局部和全局的图文关系融合。此外,为提升关系挖掘效率,采用了基于注意力机制的连边权重剪枝策略,用以增强重要分支表示,减少冗余信息干扰。在公开的Flickr30K、MSCOCO-1K、MSCOCO-5K数据集上进行方法实验,并与11种方法进行实验结果的对比分析,所提方法在Flickr30K上的平均召回率提高了0.97%和0.57%,在MSCOCO-1K上的平均召回率提高了0.93%和0.63%,在MSCOCO-5K上的平均召回率提高了0.37%和0.93%,实验结果验证了所提方法的有效性。

药食同源中药治疗胃癌癌因性疲乏的用药规律

目的:通过对文献的数据挖掘,初步探讨干预胃癌癌因性疲乏(CRF)的药食同源中药用药规律,并结合CRF中医病机及现代药理学研究进行分析。方法:检索国家知识基础设施数据库(CNKI)、中国学术期刊数据库(CSPD),筛选自建立数据库开始至2022年4月10日期间治疗胃癌CRF的临床文献,借助Microsoft Excel 2021及SPSS Modeler 26.0统计软件,以2018年卫健委公布的110种药食同源中药为参考,对收集文献所涉及的药食同源中药进行频数频率、性味归经、药效分类、关联规则分析、聚类分析,根据统计结果探讨药食同源中药干预胃癌CRF的用药规律。结果:收集文献中共有药食同源中药43味(占2018年卫健委所公布110种药食同源中药的39.09%),出现频数376次(占文献全部165味中药出现频数的49.41%);频数≥10次的药食同源中药共14味,按频数降序排列依次为黄芪、茯苓、当归、甘草、党参等;干预胃癌CRF的药食同源中药四气以平、温为主,五味以甘味药为主,归经以归脾经最多;发掘出21组治疗胃癌CRF的药食同源中药组合,支持度最高的组合是黄芪-当归;聚类分析提取获得3个核心聚类群。结论:药食同源中药干预胃癌CRF以健脾和胃为基本治则,辅以补肺滋肾,养血生津,理气燥湿为法,常用药食同源中药为黄芪、茯苓、当归等,可将这些药食同源中药用于胃癌CRF的治疗及保健食品的开发中。

基于模糊聚类的网络敏感数据流动态挖掘

为解决网络敏感数据流相似性较高且差异性较大,导致挖掘量下降的问题,该文设计基于模糊聚类的网络敏感数据流动态挖掘方法。构建模糊矩阵,计算不同网络敏感数据流样本间马氏距离,衡量两个样本间相似性,消除变量之间的差异性。引入速度收敛阈值,根据不同数据间模糊关系,对网络敏感数据流进行动态分类。利用遗传迭代算法迭代处理敏感数据流最大散度,获取最优离散性迭代值,通过对特征点进行聚类和均匀分配,结合动态挖掘误差拟合实现数据动态挖掘流。由实验结果可知,该方法挖掘到的数据量较多,无论是相似性数据还是差异性数据均能够被全部挖掘出来,挖掘结果具备可靠性。

矿区排土场植物多样性及其与土壤理化指标的耦合关系

探究鄂尔多斯满来梁矿区不同修复年限下植物多样性及其与土壤理化指标的耦合关系,揭示植被与土壤在生态修复中的相互作用,为优化植被恢复策略及评估修复效果提供科学依据。以修复年限在2~12 a的植被与土壤理化指标为研究对象,原始地貌为对照,通过调查不同样地内的植被组成与土壤理化性质,利用相关性分析揭示其相互影响与作用方式。结果表明:1)研究区共调查到51种植物,以菊科、禾本科和豆科为主,三者共占植物种类总数的61%;2)植物多样性随时间变化显著,物种数量先减少后增加,Margalef和Shannon-Weiner指数先降后升,Pielou和Simpson指数无明显变化;3)植物多样性指数与土壤理化指标存在中度及以上关联,其中pH和全磷与物种多样性关联较密切;4)修复初期,植物多样性与土壤理化指标的协调度提高,随修复年限增加,协调度先升高再降低,修复6 a时协调度最高。修复措施对植物多样性及其土壤理化指标的平衡发展有积极作用,需不断优化修复措施,使之达到最佳协调水平。

投标文件相似性检测模型研究

近年来,随着工程交易规模扩大,招投标机制得到了广泛的发展和应用。但同时也带来了一系列问题,围标、串标等行为日渐突出,这不仅扰乱了公平竞争的招投标秩序,也损害了社会公众的利益。加强对这些腐败行为的检测和监管,构建公平透明的招投标市场环境,是社会发展和行业进步的必然趋势。在已有研究的基础上,通过对投标文件特征的分析,结合定性和定量的方法开展相似性研究,构建了投标文件相似性检测指标和算法模型。该模型能够全面地评估投标文件之间的相似程度,以此识别围标串标行为,同时本文基于该模型对相关投标数据进行分析,并针对投标过程中的围串标等行为提出一些监管和检测建议。

穴位刺激治疗癌因性疲乏选穴规律数据挖掘

目的采用数据挖掘技术探究穴位刺激治疗癌因性疲乏的选穴配穴规律。方法检索中国知识资源总库(CNKI)、万方数据知识服务平台(Wanfang Data)、中文科技期刊数据库(VIP)、中国生物医学文献服务系统(SinoMed)、PubMed、Web of Science、Embase建库至2023年10月13日收录的穴位刺激治疗癌因性疲乏的临床随机对照试验文献,采用Excel2021和R4.3.1对处方腧穴进行频次分析、关联规则分析和聚类分析。结果共纳入文献187篇,包含214条腧穴处方,涉及102个腧穴,使用总频次为1044。使用频次前3位腧穴为足三里、气海、关元,前3位经络为任脉、足阳明胃经和足太阴脾经;腧穴多分布在下肢部和胸腹部,交会穴为使用频次最高的特定穴,艾灸是最常用的干预措施。关联规则分析显示,足三里-气海-关元-三阴交为核心腧穴组合。对肺癌、乳腺癌、结直肠癌、妇科肿瘤和胃癌选穴分析显示,补虚要穴最常选用。聚类分析得到5组腧穴有效聚类群。结论穴位刺激治疗癌因性疲乏选穴以益气养血、扶正祛邪为主要原则,重视特定穴的使用,并根据脏腑经络辨证运用多种配穴方法以提高临床疗效。

基于数据挖掘的耳穴贴压干预癌因性疲乏的临床应用规律

目的运用数据挖掘技术探究耳穴贴压干预癌因性疲乏的临床应用规律。方法系统检索中国知网期刊全文数据库、万方学术期刊全文数据库、维普中文科技期刊数据库和中国生物医学文献服务系统,检索时间从2000年1月1日—2024年1月1日,检索有关耳穴贴压干预癌因性疲乏的临床研究文献,提取文献内容并建立数据库,运用Excel 2010进行频数分析,使用SPSS Modeler 18.0和SPSS Statistics 27.0进行关联及聚类分析。结果耳穴贴压干预癌因性疲乏多采用单纯耳穴贴压;高频耳穴为神门、脾、交感;关联规则最强的耳穴配伍是神门-脾;聚类分析将耳穴分为4大群集。结论耳穴贴压干预癌因性疲乏耳穴选取遵照“根据脏腑辨证取穴”、“依穴位功能取穴”的原则,以神门、脾、交感为主,搭配其他耳穴,治疗方法以单纯耳穴贴压或耳穴贴压配合中药为主。目前,耳穴贴压的操作流程存在较大差异,有待进一步规范。

基于数据挖掘技术优化DRG临床用药目录

目的 优化疾病诊断相关分组(DRG)的临床用药目录,减少患者药品费用,提高DRG结付率。方法 选取某院神经内科BR23疾病组作为研究对象,应用数据挖掘技术,探索疾病组的用药规律,并利用药品综合评价方法对重点监测药品进行评分,进而优化疾病组的临床用药目录。选取2022年12月入组该疾病组患者的住院信息作为优化前数据,2023年9月入组该疾病组患者的住院信息作为优化后数据,通过比较两组患者的医疗质量及药品费用数据来评价优化目录的实施效果。结果 优化临床用药目录后,该疾病组的结付率由优化前的84.36%上升至104.70%,住院药品费用及住院总费用均显著降低(P<0.05),重点监测药品使用量明显下降。结论 数据挖掘技术有助于探索疾病组临床用药规律;药师可以此为依据,通过有效药学干预手段提高DRG结付率。

面向矿井提升机的故障知识图谱构建与应用

针对矿井提升机故障领域公开数据较少、故障知识难以被有效利用的问题,提出了一种矿井提升机故障知识图谱构建方法。该方法引入故障文本分类流程处理目标语料存在的信息冗余问题;利用词典嵌入BERT和BiLSTM-CRF结合进行实体识别,通过ERNIE进行实体关系抽取,并将抽取到的三元组存储在Neo4j图数据库中,在此基础上,实现了一个基于矿井提升机故障知识图谱的智能问答系统。该知识图谱能够较好地揭示矿井提升机故障间的复杂关联关系,实现相关故障的根因分析,为矿井提升机故障诊断提供支撑。

黑龙江省名中医秦克力教授治疗疼痛类疾病的用药规律分析

目的通过数据挖掘的方法探究黑龙江省名中医秦克力教授治疗疼痛类疾病处方的用药规律,从而为中医临床治疗疼痛类疾病提供一些客观的理论依据。方法选取黑龙江省名中医秦克力教授治疗疼痛类疾病病历中符合纳入采样标准的方剂中的中药,采样建立数据库,并分别应用数据处理软件对数据进行描述性、关联规则性、复杂网络链接关系性、系统聚类性分析,以研究黑龙江省名中医秦克力教授治疗疼痛类疾病的处方用药规律。结果本次研究共纳入了102味药物,98首方剂。其中通过对元胡、柴胡、白芍、鸡血藤、川芎、穿山龙、威灵仙等高频使用的药物,分析得到关联规则48条,分析复杂网络连接关系得到强链接3组、中等链接15组,分析系统聚类得到常用方组6组。结论黑龙江省名中医秦克力教授处方治疗疼痛类疾病辨证论治、表里同施、邪正兼顾。