Strong Association Rules Research Articles

Knowledge Discovery and Diagnosis Using Temporal-Association-Rule-Mining-Based Approach for Threshing Cylinder Blockage

Accurately diagnosing blockages in a threshing cylinder is crucial for ensuring efficiency and quality in combine harvester operations. However, in terms of blockage diagnostic methods, the current state of affairs is characterized by model-based approaches that can be highly time-consuming and difficult to implement, while data-driven approaches lack interpretability. To address this situation, we propose a temporal association rule mining (TARM)-based fault diagnosis method for identifying threshing cylinder blockages and discovering knowledge. This study performs field trials by varying the actual feed rate and obtains datasets for three blockage classes (slight, moderate, and severe). Firstly, a symbolic aggregate approximation (SAX) method is employed to reduce the data dimensionality and to construct the transaction set with a sliding window. Next, a cSpade method is used to mine and extract strong association rules by applying improved support, confidence, and lift indicators. With the established strong association rules, this study can comprehensively elucidate the variation pattern of each characteristic under several blockage failure conditions and can effectively identify blockage faults. The results demonstrate that the proposed method effectively distinguishes between three levels of blockage faults, achieving an overall diagnostic accuracy of 0.94. And the method yields precisions of 0.90, 0.92, and 0.99 and corresponding recalls of 0.90, 0.93, and 0.98 for slight, medium, and severe levels of blockage faults, respectively. Specifically, the knowledge acquired from the extracted strong association rules can effectively explain the operational characteristics of a combine harvester when its threshing cylinders are blocked. Furthermore, the proposed approach in this study can provide a reasonable and reliable reference for future research on threshing cylinder blockages.

Semantic Retrieval of Remote Sensing Images Based on the Bag-of-Words Association Mapping Method

With the increasing demand for remote sensing image applications, extracting the required images from a huge set of remote sensing images has become a hot topic. The previous retrieval methods cannot guarantee the efficiency, accuracy, and interpretability in the retrieval process. Therefore, we propose a bag-of-words association mapping method that can explain the semantic derivation process of remote sensing images. The method constructs associations between low-level features and high-level semantics through visual feature word packets. An improved FP-Growth method is proposed to achieve the construction of strong association rules to semantics. A feedback mechanism is established to improve the accuracy of subsequent retrievals by reducing the semantic probability of incorrect retrieval results. The public datasets AID and NWPU-RESISC45 were used to validate these experiments. The experimental results show that the average accuracies of the two datasets reach 87.5% and 90.8%, which are 22.5% and 20.3% higher than VGG16, and 17.6% and 15.6% higher than ResNet18, respectively. The experimental results were able to validate the effectiveness of our proposed method.

Identification of status quo and association rules for chronic comorbidity among Chinese middle-aged and older adults rural residents.

Chronic comorbidity has become a major challenge in chronic disease prevention and control. This issue is particularly pronounced in rural areas of developing countries, where the prevalence of chronic disease comorbidity is high, especially among middle-aged and older adults populations. However, the health status of middle-aged and older adults individuals in rural areas of China has received inadequate attention. Therefore, it is crucial to investigate the correlation among chronic diseases to establish a reference basis for adjusting health policies aimed at promoting the prevention and management of chronic diseases among middle-aged and older adults individuals. This study selected 2,262 middle-aged and older adults residents aged 50 years or older in Shangang Village, Jiangsu Province, China, as the study population. To analyze the chronic comorbidity of middle-aged and older adults residents with different characteristics, we used the χ2 test with SPSS statistical software. Data analysis was conducted using the Apriori algorithm of Python software, set to mine the strong association rules of positive correlation between chronic disease comorbidities of middle-aged and older adults residents. The prevalence of chronic comorbidity was 56.6%. The chronic disease comorbidity group with the highest prevalence rate was the lumbar osteopenia + hypertension group. There were significant differences in the prevalence of chronic disease comorbidity among middle-aged and older adults residents in terms of gender, BMI, and chronic disease management. The Apriori algorithm was used to screen 15 association rules for the whole population, 11 for genders, and 15 for age groups. According to the order of support, the most common association rules of comorbidity of three chronic diseases were: {lumbar osteopenia} → {hypertension} (support: 29.22%, confidence: 58.44%), {dyslipidemia} → {hypertension} (support: 19.14%, confidence: 65.91%) and {fatty liver} → {hypertension} (support: 17.82%, confidence: 64.17%). The prevalence of chronic comorbidity among middle-aged and older adults rural residents in China is relatively high. We identified many association rules among chronic diseases, dyslipidemia is mostly the antecedent, and hypertension is primarily the result. In particular, the majority of comorbidity aggregation patterns consisted of hypertension and dyslipidemia. By implementing scientifically-proven prevention and control strategies, the development of healthy aging can be promoted.

Study on the explicitation of implicit knowledge and the construction of knowledge graph on moxibustion in medical case records of ZHOU Mei-sheng's Jiusheng

To explore the methods of the explicitation of implicit knowledge and the construction of knowledge graph on moxibustion in medical case records of ZHOU Mei-sheng's Jiusheng. The medical case records data of Jiusheng was collected, the frequency statistic was analyzed based on Python3.8.6, complex network analysis was performed using Gephi9.2 software, community analysis was performed by the ancient and modern medical case cloud platform V2.3.5, and analysis and verification of correlation graph and weight graph were proceed by Neo4j3.5.25 image database. The disease systems with frequency≥10 % were surgery, ophthalmology and otorhinolaryngology, locomotor, digestive and respiratory systems. The diseases under the disease system were mainly carbuncle, arthritis, lumbar disc herniation and headache. The commonly used moxibustion methods were fumigating moxibustion, blowing moxibustion, direct moxibustion and warming acupuncture. The core prescription of points obtained by complex network analysis included Yatong point, Zhiyang(GV 9), Sanyinjiao(SP 6), Dazhui(GV 14), Zusanli(ST 36), Lingtai(GV 10), Xinshu(BL 15), Zhijian point and Hegu(LI 4), which were basically consistent with high-frequency points. A total of 6 communities were obtained by community analysis, corresponding to different diseases. Through the analysis of correlation graph, 13 pairs of strong association rule points were obtained. The correlation between Zhiyang(GV 9)-Dazhui(GV 14) and Yatong point-Lingtai(GV 10) was the strongest. The acupoints with high correlation with Yatong point were Zhiyang(GV 9), Lingtai(GV 10), Dazhui(GV 14), Zusanli(ST 36) and Sanyinjiao(SP 6). In the weight graph of the high-frequency disease system, the relationship of the first weight of the surgery system disease was fumigating moxibustion-carbuncle-Yatong point, and the relationship of the first weight of the ophthalmology and otorhinolaryngology system disease was blowing moxibustion-laryngitis-Hegu (LI 4). The results of correlation graph and weight graph are consistent with the results of data mining, which can be used as an effective way to study the knowledge base of moxibustion diagnosis and treatment in the future.

Study on the explicitation of implicit knowledge and the construction of knowledge graph on moxibustion in medical case records of ZHOU Mei-sheng's Jiusheng

To explore the methods of the explicitation of implicit knowledge and the construction of knowledge graph on moxibustion in medical case records of ZHOU Mei-sheng's Jiusheng. The medical case records data of Jiusheng was collected, the frequency statistic was analyzed based on Python3.8.6, complex network analysis was performed using Gephi9.2 software, community analysis was performed by the ancient and modern medical case cloud platform V2.3.5, and analysis and verification of correlation graph and weight graph were proceed by Neo4j3.5.25 image database. The disease systems with frequency≥10 % were surgery, ophthalmology and otorhinolaryngology, locomotor, digestive and respiratory systems. The diseases under the disease system were mainly carbuncle, arthritis, lumbar disc herniation and headache. The commonly used moxibustion methods were fumigating moxibustion, blowing moxibustion, direct moxibustion and warming acupuncture. The core prescription of points obtained by complex network analysis included Yatong point, Zhiyang(GV 9), Sanyinjiao(SP 6), Dazhui(GV 14), Zusanli(ST 36), Lingtai(GV 10), Xinshu(BL 15), Zhijian point and Hegu(LI 4), which were basically consistent with high-frequency points. A total of 6 communities were obtained by community analysis, corresponding to different diseases. Through the analysis of correlation graph, 13 pairs of strong association rule points were obtained. The correlation between Zhiyang(GV 9)-Dazhui(GV 14) and Yatong point-Lingtai(GV 10) was the strongest. The acupoints with high correlation with Yatong point were Zhiyang(GV 9), Lingtai(GV 10), Dazhui(GV 14), Zusanli(ST 36) and Sanyinjiao(SP 6). In the weight graph of the high-frequency disease system, the relationship of the first weight of the surgery system disease was fumigating moxibustion-carbuncle-Yatong point, and the relationship of the first weight of the ophthalmology and otorhinolaryngology system disease was blowing moxibustion-laryngitis-Hegu (LI 4). The results of correlation graph and weight graph are consistent with the results of data mining, which can be used as an effective way to study the knowledge base of moxibustion diagnosis and treatment in the future.探究周楣声《灸绳》医案灸法隐性知识显性化及其知识图谱构建的方法。收集《灸绳》医案数据，基于Python3.8.6进行频数统计分析，利用Gephi9.2软件进行复杂网络分析，登录古今医案云平台V2.3.5进行社团分析，并运用Neo4j3.5.25图数据库进行关联图谱和权值图谱的分析及验证。出现频率≥10%的疾病系统分别为外科、五官、运动、消化和呼吸系统。疾病系统下属疾病以痈、关节炎、腰椎间盘突出症、头痛为主。治疗常用灸法为熏灸、吹灸、直接灸和温针灸。复杂网络分析所得腧穴核心组方为压痛穴、至阳、三阴交、大椎、足三里、灵台、心俞、指尖、合谷，基本与高频腧穴相一致。社团分析共得到6个社团，分别与不同疾病相对应。关联图谱分析得到强关联规则穴对13条，至阳-大椎、压痛穴-灵台的关联性最强，与压痛穴关联性较高的腧穴为至阳、灵台、大椎、足三里、三阴交。高频疾病系统权值图谱中，外科系统疾病权值排名第一的关系为熏灸-痈-压痛穴，五官系统疾病权值排名第一的关系为吹灸-喉炎-合谷。关联图谱及权值图谱所得结论与数据挖掘结果相一致，可作为未来研究灸法诊疗知识库的有效途径。.

Mining and Utilizing Knowledge Correlation and Learners’ Similarity Can Greatly Improve Learning Efficiency and Effect: A Case Study on Chinese Writing Stroke Correction

Using AI technology to improve teaching and learning is an important goal of educational sustainability. By mining the correlation between knowledge points, the discrete knowledge points can be integrated to improve the knowledge density and reduce the learning task. In addition, the successful experiences of similar learners can be shared, thus shortening the learning path of new learners. To change the common situation of irregular writing stroke order, to teach and correct stroke order effectively, this study uses association rules to explore the potential correlation between error-prone Chinese characters based on a large number of learners’ writing records, and then summarizes and sorts out a set of error-prone Chinese characters based on this. Every Chinese character contained in an error-prone category has a common error-prone feature. By correcting this error, it can be extended to every Chinese character of this category, and the learning efficiency of Chinese character strokes can be improved tens of times. In the training and testing system with a Chinese character error-prone character set, combined with the improved collaborative filtering algorithm, a learner-based personalized error-prone Chinese character recommendation model was proposed. Experimental results showed that the Apriori algorithm with lift measure can excavate effective strong association rules and provide an important reference for the character set table. The improved collaborative filtering algorithm can make use of the similarity between learners, share successful learning experiences, provide a personalized recommendation service for error-prone Chinese characters, and the recommendation performance is higher than that of the traditional collaborative filtering model. In the test of different types of learning groups, there are obvious differences between the independent pre-test and the post-training test, which effectively corrects the irregular writing habits, and further indicates that the excavation of knowledge correlation and the combination of learners’ similarity can effectively improve the efficiency and effect of teaching and learning.

Using improved feature extraction combined with RF-KNN classifier to predict coal and gas outburst

Accurate and rapid prediction of the coal and gas outburst is very significant for preventing accident and protecting environment, the paper presents a novel feature selection and outburst classifier framework which can identify effective candidate features and improve the classification accuracy. First, Apriori is applied for preliminarily extracting the association rules from sample data and attribute features in coal and outburst, and it can present the effective sample data and features for outburst prediction. Second, in order to reduce the redundancy of the strong association rules obtained from Apriori, Boruta is applied for selecting all highly relevant optimal features based on the obtained strong association rules. Third, Random Forest(RF) is used to assign different weights to different features in optimal candidate features considering the importance of different features to outburst, based on the above obtained high-quality sample data and optimal features, the parameters of KNN model optimized by Bayesian Optimization(BO) is used to predict the coal and gas outburst. The experimental results show that the proposed feature selection model Apriori-Boruta can obtain significant sample data, and the proposed RF- KNN optimized classifier model can achieve higher performance in terms of the number of optimal features and prediction accuracy compared with traditional prediction models.

Power Incomplete Data Clustering Based on Fuzzy Fusion Algorithm

With the rapid development of the economy, the scale of the power grid is expanding. The number of power equipment that constitutes the power grid has been very large, which makes the state data of power equipment grow explosively. These multi-source heterogeneous data have data differences, which lead to data variation in the process of transmission and preservation, thus forming the bad information of incomplete data. Therefore, the research on data integrity has become an urgent task. This paper is based on the characteristics of random chance and the Spatio-temporal difference of the system. According to the characteristics and data sources of the massive data generated by power equipment, the fuzzy mining model of power equipment data is established, and the data is divided into numerical and non-numerical data based on numerical data. Take the text data of power equipment defects as the mining material. Then, the Apriori algorithm based on an array is used to mine deeply. The strong association rules in incomplete data of power equipment are obtained and analyzed. From the change trend of NRMSE metrics and classification accuracy, most of the filling methods combined with the two frameworks in this method usually show a relatively stable filling trend, and will not fluctuate greatly with the growth of the missing rate. The experimental results show that the proposed algorithm model can effectively improve the filling effect of the existing filling methods on most data sets, and the filling effect fluctuates greatly with the increase of the missing rate, that is, with the increase of the missing rate, the improvement effect of the model for the existing filling methods is higher than 4.3%. Through the incomplete data clustering technology studied in this paper, a more innovative state assessment of smart grid reliability operation is carried out, which has good research value and reference significance.

Regional Geological Disasters Emergency Management System Monitored by Big Data Platform

In order to deal with the hazards caused by geological disasters in time, an emergency management system is proposed based on association rule data mining. With the support of a big data platform, a regional geological disaster emergency management system is built based on monitoring data. In the result analysis, the association rule algorithm demonstrates high computing power in the test, which can filter the data with strong association rules. In addition, the big data platform can allow data visualization, which has good data storage capacity and disaster early warning capacity. In the simulation test of the emergency management system, it was found that the system is feasible in theory. When it is applied to the actual disaster emergency management, it wasfound that, in the face of geological disasters, the processing speed of relevant departments increased by 59.4%, and the allocation of personnel and materials wasmore reasonable. The above results show that the big data platform monitoring data can improve the regional geological disasters emergency management capacity and ensure the safety of people’s lives and property.

A New Text-Mining–Bayesian Network Approach for Identifying Chemical Safety Risk Factors

The frequent occurrence of accidents in the chemical industry has caused serious economic loss and negative social impact. The chemical accident investigation report is of great value for analyzing the risk factors involved. However, traditional manual analysis is time-consuming and labor-intensive, while existing keyword extraction methods still need to be improved. This study aims to propose an improved text-mining method to analyze a large number of chemical accident reports. A workflow was designed for building and updating lexicons of word segmentation. An improved keyword extraction algorithm was proposed to extract the top 100 keywords from 330 incident reports. A total of 51 safety risk factors was obtained by standardizing these keywords. In all, 294 strong association rules were obtained by Apriori. Based on these rules, a Bayesian network was built to analyze safety risk factors. The mean accuracy and mean recall of the BM25 model in the comparison experiments were 10.5% and 14.38% higher than those of TF-IDF, respectively. The results of association-rule mining and Bayesian network analysis can clearly demonstrate the interrelationship between the safety risk factors. The methodology of this study can quickly and efficiently extract key information from incident reports which can provide managers with new insights and suggestions.

Disaster Precursor Identification and Early Warning of the Lishanyuan Landslide Based on Association Rule Mining

It is the core prerequisite of landslide warning to mine short-term deformation patterns and extract disaster precursors from real-time and multi-source monitoring data. This study used the sliding window method and gray relation analysis to obtain features from multi-source, real-time monitoring data of the Lishanyuan landslide in Hunan Province, China. Then, the k-means algorithm with particle swarm optimization was used for clustering. Finally, the Apriori algorithm is used to mine strong association rules between the high-speed deformation process and rainfall features of this landslide to obtain short-term deformation patterns and precursors of the disaster. The data mining results show that the landslide has a high-speed deformation probability of more than 80% when rainfall occurs within 24 h and the cumulative rainfall is greater than 130.60 mm within 7 days. It is of great significance to extract the short-term deformation pattern of landslides by data mining technology to improve the accuracy and reliability of early warning.

Identification of Environmental Pollutants in Construction Site Monitoring Using Association Rule Mining and Ontology-Based Reasoning

Pollutants from construction activities of building projects can have serious negative impacts on the natural environment and human health. Carrying out monitoring of environmental pollutants during the construction period can effectively mitigate environmental problems caused by construction activities and achieve sustainable development of the construction industry. However, the current environmental monitoring method relying only on various sensors is relatively singlar which is unable to cope with a complex on-site environment We propose a mechanism for environmental pollutants identification combining association rule mining and ontology-based reasoning and using random forest algorithm to improve the accuracy of identification. Firstly, the ontology model of environmental pollutants monitoring indicator in the construction site is built in order to integrate and share the relative knowledge. Secondly, the improved Apriori algorithm with added subjective and objective constraints is used for association rule mining among environmental pollutants monitoring indicators, and the random forest algorithm is applied to further filter the strong association rules. Finally, the ontology database and rule database are loaded into a Jena reasoning machine for inference to establish an identification mechanism of environmental pollutants. The results of running on a real estate development project in Jiangning District, Nanjing, prove that this identification mechanism can effectively tap the potential knowledge in the field of environmental pollutants monitoring, explore the relationship between environmental pollutants monitoring indicators and then overcome the shortcomings of traditional monitoring methods that only rely on sensors to provide new ideas and methods for making intelligent decisions on environmental pollutants in a construction site.

A Study on the Identification of Delayed Delivery Risk Transmission Paths in Multi-Variety and Low-Volume Enterprises Based on Bayesian Network

Due to the prevalence of the order production mode, multi-variety, small-batch manufacturing enterprises frequently delay deliveries to downstream customers. To date, most existing studies on delayed delivery risk have focused on the response to the risk after it occurs, ignoring how the risk arises. For multi-variety, low-volume production companies, any part of the production process could lead to the ultimate risk of delayed delivery, and the risk is transmissible. Therefore, the path of risk transmission needs to be identified to effectively control the risk of late delivery at key production stages. In this paper, from the perspective of risk transmission, a recognition method based on association rules and the Bayesian network was proposed to identify the risk conduction path. This method firstly determined the strong association rules among the risk factors based on historical data stored in the ERP system and determined the Bayesian network topology structures of the risk transmission path by combining the business process and expert experience. Secondly, the prior and conditional probabilities of each node were determined using data statistics, and the risk transmission path of delayed delivery was identified using the forward and backward reasoning of the Bayesian network. Finally, this paper provided a case study to verify the method, and the following conclusions were obtained: (1) the delay in delivery to downstream customers is mainly due to the delayed delivery of upstream suppliers and the sudden change in customer demand, and (2) the adjustment of enterprise production plans is the key node of the delayed delivery risk transmission path. Through the research in this paper, production companies can identify the target of risk management more scientifically and mitigate the risk through the adjustment of key links.

Dental X-ray Identification System Based on Association Rules Extracted by k-Symbol Fractional Haar Functions

Several identification approaches have recently been employed in human identification systems for forensic purposes to decrease human efforts and to boost the accuracy of identification. Dental identification systems provide automated matching by searching photographic dental features to retrieve similar models. In this study, the problem of dental image identification was investigated by developing a novel dental identification scheme (DIS) utilizing a fractional wavelet feature extraction technique and rule mining with an Apriori procedure. The proposed approach extracts the most discriminating image features during the mining process to obtain strong association rules (ARs). The proposed approach is divided into two steps. The first stage is feature extraction using a wavelet transform based on a k-symbol fractional Haar filter (k-symbol FHF), while the second stage is the Apriori algorithm of AR mining, which is applied to find the frequent patterns in dental images. Each dental image’s created ARs are saved alongside the image in the rules database for use in the dental identification system’s recognition. The DIS method suggested in this study primarily enhances the Apriori-based dental identification system, which aims to address the drawbacks of dental rule mining.

Optimized Apriori algorithm for deformation response analysis of landslide hazards

With the explosive development of data mining technology, higher requirements have been put forward to analyze the response of landslide deformation. However, related algorithms, such as the traditional Apriori and FP-Growth algorithms, are still in the starting period of being applied to landslide hazards. Due to monitoring data characteristics, some problems were encountered while applying these algorithms, such as poor applicability and low computational efficiency. Therefore, we propose an optimized Apriori algorithm to solve the above problems. The optimized algorithm strictly controls the front and rear itemsets' construction process and stores the factors and deformation events according to their dimensions and levels. In addition, some key calculation processes of the algorithm are well-parallelized. Based on the monitoring data of the Baishuihe landslide, three experiments were designed to verify the performance of the proposed algorithm. The results show that when the strong association rules with high factor dimension and level characteristics were obtained, the proposed algorithm's computation time was 1/432 and 1/80 of the Apriori and FP-Growth algorithms, respectively. The proposed algorithm has significant advantages in analyzing massive high-dimensional monitoring data of landslide hazards.

ARHap: Association Rule Haplotype Phasing.

This article proposes a novel approach for Individual Human phasing through discovery of interesting hidden relations among single variant sites. The proposed framework, called ARHap, learns strong association rules among variant loci on the genome and develops a combinatorial approach for fast and accurate haplotype phasing based on the discovered associations. ARHap is composed of two main modules or processing phases. In the first phase, called association rule learning, ARHap identifies quantitative association rules from a collection of DNA reads of the organism under study, resulting in a set of strong rules that reveal the inter-dependency of alleles. In the next phase, called haplotype reconstruction, we develop algorithms to utilize the learned rules to construct highly reliable haplotypes at individual single nucleotide polymorphism (SNP) sites. ARHap has several features that lead to both fast and accurate haplotyping. It uses an incremental haplotype reconstruction approach that enables us to generate association rules according to the unreconstructed SNP sites during each round of the algorithm. During each round, the association rule learning module generates rules while constraining the length of the rules and limiting the rules to those that contribute to reconstruction of unreconstructed sites only. The framework begins by generating rules of small size and highly strong. The rule length can increase and/or criteria about strongness of the rule are adjusted gradually, during subsequent rounds, if some SNP sites have remained unreconstructed. This adaptive approach, which uses feedback from haplotype reconstruction module, eliminates generation of rules that do not contribute to haplotype reconstruction as well as weak rules that may introduce error in the final haplotypes. Extensive experimental analyses on datasets representing diploid organisms demonstrate superiority of ARHap in diploid haplotyping compared to the state-of-the-art algorithms. In particular, we show that this novel approach to haplotype phasing not only is fast but also achieves significantly better accuracy performance compared to other read-based computational approaches.

Rule base construction method of section out of limit disposal strategy for thermal power unit equipment fault

This paper proposes a rule base construction method of section out of limit disposal strategy for thermal power unit equipment fault, which can solve the problems of abnormal power transmission and grid real-time scheduling caused by section out of limit. Through the improved Apriori algorithm, the section out of limit disposal strategy of thermal power unit equipment fault is designed. In the mining result of thermal power unit equipment fault section out of limit, the association rules between section state and unit and load disposal strategy are established. By using the improved Apriori algorithm, the policy data and section state data are set as the preceding and following constraints of the association rules, The strong association rules are obtained, and the rule base of cross section out of limit disposal strategy is constructed according to the association rules. The experimental results show that: the number of association rules obtained by the improved Apriori algorithm mining each database is less than 1000, and the rule effectiveness and analysis efficiency are significantly improved; The rule base built by this method can effectively deal with the out of limit problems of different sections and units, and the sensitivity of each generator node increases greatly after the treatment.

Representation, mining and analysis of unsafe behaviour based on pan-scene data.

To describe the safety rules of various industrial process data and explore the characteristics of unsafe behaviour, the association rules of unsafe behaviour based on pan-scene were proposed in this study. First, based on the scene data theory, unsafe behaviour was described by eight dimensions (time, location, behavioural individual, unsafe action, behavioural attribute, behavioural trace, professional category and risk level) to achieve scene data description and structural transformation. Second, the Apriori algorithm was used to explore the distribution rules of unsafe behaviour dimensions and the interaction between different dimensions from two perspectives: single-dimensional statistical analysis and multidimensional association rule mining. Finally, through SPSS Modeler software, an empirical analysis of pan-scene data for subway construction was conducted, and the association rules between type of work, construction stage, working time and unsafe action were identified. Some strong association rules were produced by the association analysis. For example, during the 13:00–17:00 of the excavation floor stage, the most frequent unsafe action of machine operators is the irregular binding of lifting objects. This result could explain why some unsafe actions are prone to occur in different construction stages and working times for workers of different types, which can be controlled and managed in a targeted manner, thus reducing the possibility of accidents.

Association discovery and outlier detection of air pollution emissions from industrial enterprises driven by big data

ABSTRACT Air pollution is a major issue related to national economy and people's livelihood. At present, the researches on air pollution mostly focus on the pollutant emissions in a specific industry or region as a whole, and is a lack of attention to enterprise pollutant emissions from the micro level. Limited by the amount and time granularity of data from enterprises, enterprise pollutant emissions are still understudied. Driven by big data of air pollution emissions of industrial enterprises monitored in Beijing-Tianjin-Hebei, the data mining of enterprises pollution emissions is carried out in the paper, including the association analysis between different features based on grey association, the association mining between different data based on association rule and the outlier detection based on clustering. The results show that: (1) The industries affecting NOx and SO2 mainly are electric power, heat production and supply industry, metal smelting and processing industries in Beijing-Tianjin-Hebei; (2) These districts nearby Hengshui and Shijiazhuang city in Hebei province form strong association rules; (3) The industrial enterprises in Beijing-Tianjin-Hebei are divided into six clusters, of which three categories belong to outliers with excessive emissions of total VOCs, PM and NH3 respectively.

Research on causes of coal mine gas explosion accidents based on association rule

Statistical analysis of accidents is an important part of safety management and accident prevention. In this study, the case information of gas explosion accidents was analysed to deeply explore the accident characteristics. A total of 125 coal mine gas explosion accident reports in China from 2010 to 2020 were considered as the original data, and 30 causal factors of gas explosion accidents were extracted from the macro, meso, and micro levels. The Carma algorithm was used to mine the frequent itemsets of the sample data. The potential regular characteristics of gas explosion accidents were further explored by determining the association rules between the causal factors of the itemsets. The results show that the probability of gas explosion accidents caused by the violation of operating procedures, disordered ventilation systems, or illegal production organisations is high. Many strong association rules generated by human-management coupling were observed. The strong association rules of multi-factor coupling mainly include human-equipment-management coupling. This study provides new ideas for the prevention of gas explosion accidents.