Association Rule Mining Method Research Articles

Accident black spots identification based on association rule mining

This paper presents an analytical approach to identifying the important characteristics of accident black spots on Moroccan rural roads. An association rule mining method is applied to extract road spatial characteristics associated with fatal accidents. The weighted severity index was calculated for each section, which was then used to determine the severity levels of black spots. The apriori algorithm is applied to find the correlation between road characteristics and the severity levels of black spots. Then, a general rule selection method is proposed to identify the rules strongly associated with each severity level. The results show that the proposed approach is effective in identifying the most important factors contributing to accidents. Furthermore, it shows that the combination of several road characteristics, such as road width, road surface, and bridge presence, may contribute to fatal accidents. The general rule selection found that wet, bad surfaces, and narrow shoulders were significantly associated with accidents on rural roads. The findings of the present study can help develop effective strategies to reduce road accidents and thus improve road safety in the country.

Enhancing Power Grid Data Analysis with Fusion Algorithms for Efficient Association Rule Mining in Large-Scale Datasets

Against the backdrop of the rapid development of information technology, the total amount of data has exploded, and efficient association rule mining methods for large-scale datasets have been studied. Conventional rule mining algorithms are subject to electrical constraints when working, and their convergence speed and data noise are currently the main problems they face. In order to accelerate the working process of the algorithm, this study introduces a data warehouse into the K-Means algorithm, and connects the time series and voltage interaction functions with the long-and-short-term memory network for efficient information analysis of power grid data, generating fusion algorithms. The study conducted experiments on the Netloss dataset and simultaneously conducted experiments on three models, including long-and-short-term memory networks, to verify the superiority of the fusion algorithm. Under the same experimental voltage, the circuit power flows of the four models were 0.37, 0.64, 0.79, and 0.82A, respectively, indicating that the algorithm effectively controlled the electrical dataset. Its measurement accuracy was the highest among the four models, at 91.7%. The experimental results show that the fusion algorithm proposed in the study has precise control ability in power grid datasets, and can effectively mine association rules on large-scale datasets.

Demystifying COVID-19 mortality causes with interpretable data mining

While COVID-19 becomes periodical, old individuals remain vulnerable to severe disease with high mortality. Although there have been some studies on revealing different risk factors affecting the death of COVID-19 patients, researchers rarely provide a comprehensive analysis to reveal the relationships and interactive effects of the risk factors of COVID-19 mortality, especially in the elderly. Through retrospectively including 1917 COVID-19 patients (102 were dead) admitted to Xiangya Hospital from December 2022 to March 2023, we used the association rule mining method to identify the risk factors leading causes of death among the elderly. Firstly, we used the Affinity Propagation clustering to extract key features from the dataset. Then, we applied the Apriori Algorithm to obtain 6 groups of abnormal feature combinations with significant increments in mortality rate. The results showed a relationship between the number of abnormal feature combinations and mortality rates within different groups. Patients with “C-reactive protein > 8 mg/L”, “neutrophils percentage > 75.0 %”, “lymphocytes percentage < 20%”, and “albumin < 40 g/L” have a 2×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} mortality rate than the basic one. When the characteristics of “D-dimer > 0.5 mg/L” and “WBC > 9.5×109\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$9.5 \ imes 10^9$$\\end{document} /L” are continuously included in this foundation, the mortality rate can be increased to 3×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} or 4×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document}. In addition, we also found that liver and kidney diseases significantly affect patient mortality, and the mortality rate can be as high as 100%. These findings can support auxiliary diagnosis and treatment to facilitate early intervention in patients, thereby reducing patient mortality.

Apply and improve the association rules in predicting national high school admission scores to Nha Trang University

The paper discusses how the traditional method of association rule mining based on user-defined minimum support and confidence can result in either too many or too few association rules. This could lead to valuable information being missed or redundant rules being generated, which is not practical and can be costly to implement. The paper proposes an improvement of DARIT algorithm to mine association rules without redundancy and applies it to data from previous years of National High School Exams to Nha Trang University. The goal is to use the results to build an admissions counseling system that can help students increase their chances of being admitted to universities based on their exam results. Keywords: Data mining, Association rule, Missing value, DARIT, consulting system.

Source Domain Associations as Conceptual Assemblages in Trauma Talk – an Association Rule Mining Approach

ABSTRACT The qualitative nature of co-deployed or “associated” metaphorical source domains in discourse has been extensively researched, often in terms of whether they share common conceptual roots. There are however few empirical studies on the strength of these associations and their implications. In mental healthcare activities like psychological interviews and counseling, for example, strongly associated sources may suggest unique “conceptual assemblages” that highlight underexplored (dis)similarities between clients or client groups, beyond the typical focus on isolated sources and frequencies. Our case study compares source domain associations produced by two interviewee groups – one meeting the diagnostic threshold for acute stress disorder (ASD), the other not – relating their first-hand experiences of traumatic events during the 2019 Hong Kong social unrest. The machine learning method of association rule mining is used to extract and quantify the top association rules in both groups. Results show i) a shared rule suggesting a highly schematic construal of trauma that nevertheless varies in instantiating details, and ii) several distinct rules that corroborates and lends further insight into the “agentive vs. non-agentive” characteristics exhibited by ASD vs. non-ASD individuals. Implications and future directions, including potential extensions to other discourse contexts, are discussed.

Association rules combined fuzzy decision quality control technology in intelligent manufacturing

In the context of the rapid development of intelligent manufacturing, effective quality control has become the key to improving manufacturing efficiency and product quality. Traditional quality control methods are often inadequate when faced with complex production data and changing manufacturing environments. Therefore, exploring new intelligent quality control technologies to cope with these challenges in intelligent manufacturing has become an important research direction. In view of this, the study proposed a quality control technology that combines association rules and fuzzy decision-making. Firstly, association rule mining methods are used to analyze production data and extract the relationships between key quality factors. Secondly, based on these association rules, fuzzy decision technology is used to adjust and optimize the production process, ultimately achieving quality control of products in the intelligent manufacturing production process. The data showed that when running on the training set and validation set, the research method reached a stable state at 18 and 46 iterations of the system, respectively, with a minimum cost loss function value. In both batch production lines, the detection efficiency under the operation of the research method remained at 2200 units per minute. During the process of repeating the system for 6 times, the research method consistently achieved maximum control accuracy and minimum time consumption. The satisfaction of the four experts with the operation of the research method in small batch production and large batch production was 9.32 and 9.2, respectively, significantly higher than other algorithms. The above results indicated that the proposed method can effectively improve the efficiency of product quality control, reduce production costs, and ultimately reduce the rate of defective products in the production process.

Combined learning models for survival analysis of patients with pulmonary hypertension

Background Pulmonary hypertension is a disease that manifests itself by excessive and chronic elevation of pressure in the pulmonary arteries. Pulmonary Hypertension (PH) presents non-specific initial symptoms, and this leads to an often-late diagnosis with a prognosis that can compromise the survival of the patient.Methods This paper will propose a combined methodological approach of the survival Support Vector Machine (SVM), Cox Proportional-Hazards Model (Cox-PHM), and Association Rule Mining (ARM) methods to examine the associated factors that are critical to the survival prognosis of patients with pulmonary hypertension (PAH).Results This approach was applied to data from 171 patients with PAH from 9 hospitals in 4 sub-Saharan African countries. For the study, nine PAH-related parameters were recorded for each patient. The SVM and Cox-PHM models showed that "Associated with Schistosomiasis" was the most significant parameter on death status. These allowed the ARM model to identify all the rules related to this significant parameter to provide more necessary information, such as detecting any other parameter that may enter the game with more precision.Conclusion The interpretation of results from the combined approach offers clarifications that are useful for a good understanding of the etiological factors and influential variables in the prognosis of survival of pulmonary hypertension. This provides an additional perspective in line with the messages of recent international recommendations which suggest that early diagnosis and treatment contribute to improving the vital prognosis of patients.

Multiparameter warning of lithium-ion battery overcharge-thermal runaway

The rapid development of new energy vehicles has drawn widespread attention to battery safety. Overcharging, as an important source of thermal runaway, may occur instantaneously without obvious signs, and any corresponding fire will be difficult to extinguish. This study is an investigation of overcharging thermal runaway and thermal runaway warnings for lithium-ion batteries. A stress-type early warning system is proposed, which has faster response time and more distinctive characteristics compared with other parameters. Through the association rule mining method, a multi-parameter coupled thermal runaway early warning strategy based on voltage, temperature, and pressure parameters was designed. A hierarchical early warning model including feature extraction, data processing and early warning evaluation modules was established. On this basis, a remaining time prediction module was added to achieve an alarm escape time of up to 474 s and shortest of 65 s, meeting safety standards. In the thermal runaway experiment at 705.2 °C, the early warning level system was triggered respectively. The maximum battery temperatures were 28.4 °C, 41.5°Cand 60.3 °C. The escape time errors were 16.56 s, 11.52 s, and 11.88 s respectively, all within 20 s for each level. Corresponding to different experimental results, the significant level classification simultaneously verifies the accuracy and effectiveness of the classification strategy.

State feedback method of inter-turn short circuit in rotor winding of induction motor based on improved association rule algorithm

Aiming at timely feedback and processing of asynchronous motor rotor windings, a feedback method based on an improved association rule algorithm of asynchronous motor rotor windings is studied. Based on the inter-turn short circuit mechanism of asynchronous motor windings, the fuzzy association rule mining method is used to mine the inter-turn short circuit mechanism of asynchronous motor windings. The method mines the rotor-side electrical parameters as the input of the deep neural network and uses the deep neural network to output the short-circuit state feedback results of the rotor windings of the asynchronous motor. The experimental results show that when the A-phase of the asynchronous motor is the faulty phase, the A-phase current of the rotor winding inter-turn short circuit increases significantly, and the phase angle difference deviation has a positive correlation, which effectively feedbacks the asynchronous motor rotor winding inter-turn short-circuit state.

Implementasi Metode Association Rule Mining Pada Penjualan Barang Di Toko Bangunan Ada Mas Menggunakan Algoritma Apriori

The purpose of this research is to implement the association rule mining method on sales transaction data at the store and also to find out the most popular and most sold building materials at the Ada Mas Building Shop. The implementation of the Association Rule Mining Method on the sale of goods at the Ada Mas Building Shop is used to provide information on the results of the sales pattern of building materials every month which can be used as a parameter in managing the inventory of building materials at the Ada Mas Building Shop, besides that it can provide information on the most popular and most sold building materials at the Ada Mas Building Shop. Based on sales transaction data for September 2022 as many as 193 transactions, the results of 2 itemset sales patterns using the Association Rule Mining Method that meet the minimum support of 10% and minimum confidence of 50% are the most popular items, namely Iron 10 KSTY, Iron 8 KSTY, and Iron 6 KSTY.

A novel high-utility association rule mining method and its application to movie recommendation

A novel high-utility association rule mining method and its application to movie recommendation

Time series numerical association rule mining variants in smart agriculture

Numerical association rule mining offers a very efficient way of mining association rules, where algorithms can operate directly with categorical and numerical attributes. These methods are suitable for mining different transaction databases, where data are entered sequentially. However, little attention has been paid to the time series numerical association rule mining, which offers a new technique for extracting association rules from time series data. This paper presents a new algorithmic method for time series numerical association rule mining and its application in smart agriculture. We offer a concept of a hardware environment for monitoring plant parameters and a novel data mining method with practical experiments. The practical experiments showed the method’s potential and opened the door for further extension.

Research on the innovation of the path of ideological and political education in universities based on data mining

Abstract This paper first describes the parallel association rule mining method for the big data implementation process. To perform similar item merging, each data item in the dataset is scanned, the dataset is divided, and then the similarity degree is calculated. And the dynamic support threshold is obtained based on the idea of genetic iteration of the genetic algorithm to obtain the optimal solution. In order to conduct data mining, the mining process is then transferred to the MapReduce computing platform while the data output from the Map step is parallel LZO compressed. Finally, in the age of big data, the ideological and political work in colleges is mined and analyzed using a data mining algorithm, and route innovation remedies are proposed. According to the current state of ideological and political education, 77.6% of respondents believe that the ideological and political management system is not ideal, and 61.48 percent believe that the ideological and political content is fragmented and that data-based information makes it challenging to supervise. The immaturity of new media development and the growth of technological challenges amounted to 46%. After creative approaches, ideological and political education teaching satisfaction has grown by 15-20 percentage points. Ideological and political education at universities under the impact of big data should grab the possibilities and overcome the difficulties to achieve sustainability and innovative development.

Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method

Severe asthma is a chronic inflammatory airway disease with great therapeutic challenges. Understanding the genetic and molecular mechanisms of severe asthma may help identify therapeutic strategies for this complex condition. RNA expression data were analyzed using a combination of artificial intelligence methods to identify novel genes related to severe asthma. Through the ANOVA feature selection approach, 100 candidate genes were selected among 54,715 mRNAs in blood samples of patients with severe asthmatic and healthy groups. A deep learning model was used to validate the significance of the candidate genes. The accuracy, F1-score, AUC-ROC, and precision of the 100 genes were 83%, 0.86, 0.89, and 0.9, respectively. To discover hidden associations among selected genes, association rule mining was applied. The top 20 genes including the PTBP1, RAB11FIP3, APH1A, and MYD88 were recognized as the most frequent items among severe asthma association rules. The PTBP1 was found to be the most frequent gene associated with severe asthma among those 20 genes. PTBP1 was the gene most frequently associated with severe asthma among candidate genes. Identification of master genes involved in the initiation and development of asthma can offer novel targets for its diagnosis, prognosis, and targeted-signaling therapy.

Prediction method of peak carbon emission in power grid based on energy consumption elasticity coefficient

With the implementation of China’s “two-carbon” target strategy, the status of renewable energy in the power system is gradually improving. In order to improve the carbon emission control level of power grid, it is necessary to predict the peak carbon emission of power grid. A prediction method of grid carbon emission peak based on energy elasticity coefficient is proposed. A time series model of grid carbon emission samples was constructed, and a combination of combinatorial sorting and machine learning was used to reconstruct the time series of grid carbon emission peaks. Based on the results of time series reconstruction, feature extraction and classification training of grid carbon emission peak are carried out, and the measurement error correction of grid carbon emission under the constraint of energy consumption elasticity is realized. Combined with the association rule mining method, the fusion processing of grid carbon emission peak samples under the elastic constraint of energy consumption is realized. Through characteristic detection and fusion processing of energy consumption elasticity coefficient, the reconstituted carbon emission samples of power grid constrained by energy consumption elasticity were reconstructed, and the peak carbon emission of power grid was predicted. The simulation results show that this method has high accuracy and convergence in predicting the peak carbon emission of power grid, and improves the error correction ability in the prediction process.

Understanding Social Inequality in Individual Perceived Exposures to Air Pollution in Residential and Visited Neighborhoods: A Study Using Association Rule Mining

Using individual-level data collected from two communities in Hong Kong, this study proposes a significant association rule mining method to identify the complex associations between individual socioeconomic characteristics and perceived air pollution in people’s daily life. It defines a measure, namely the rule inequality index, to assess the social inequality in perceived exposure to air pollution in both residential and visited neighborhoods. The results indicate that the associations between individual socioeconomic characteristics and perceived air pollution are not always consistent over communities, nor are the value ranges of perceived air pollution. Further, the tendency of different social groups to perceive high levels of air pollution can differ considerably depending on whether they are in their residential or visited neighborhoods. We also find that social groups based on different socioeconomic variables typically experience varying degrees of neighborhood effects on the associated social equalities. Our findings emphasize the importance of considering nonstationary associations and human mobility in research on social inequality related to mobility-dependent environmental exposure.

Discovery of fault-introducing tool groups with a numerical association rule mining method in a printed circuit board production line

Large-scale manufacturers aim to reduce the number of faulty products by finding tools or process factors that cause product faults through tool monitoring. The causes of faulty electronic components such as semiconductor chips and printed circuit boards (PCBs) include abnormalities in single tools and abnormalities caused by interactions between the tools of a specific process and a related process. Here, the tools exhibiting an interaction effect are called the fault-introducing tool group. This study presents a numerical association rule mining method for discovering the fault-introducing tool groups based on a genetic algorithm. A novel fitness function and rule pruning process are developed to identify the fault-introducing tool groups. The effectiveness of the method is verified using simulations and a case study of actual PCB production lines. The proposed method can discover fault-introducing tool groups better than machine learning algorithms. Additionally, the method can accurately identify fault-introducing tool groups in various manufacturing environments, such as those with highly skewed yield distributions or variations in yield distributions over time. In an actual PCB production line, the groups identified by the proposed method produced up to 36.5% more faulty chips than those identified by the comparison models.

A comprehensive review of visualization methods for association rule mining: Taxonomy, challenges, open problems and future ideas

Association rule mining is intended for searching for the relationships between attributes in transaction databases. The whole process of rule discovery is very complex, and involves pre-processing techniques, a rule mining step, and post-processing, in which visualization is carried out. Visualization of discovered association rules is an essential step within the whole association rule mining pipeline, to enhance the understanding of users on the results of rule mining. Several association rule mining and visualization methods have been developed during the past decades. This review paper aims to create a literature review, identify the main techniques published in peer-reviewed literature, examine each method’s main features, and present the main applications in the field. Defining the future steps of this research area is another goal of this review paper.

Knowledge, attitude, and perception regarding COVID-19-related prevention practice among residents in Vietnam: a cross-sectional study.

Vietnam was one of the countries pursuing the goal of "Zero-COVID" and had effectively achieved it in the first three waves of the pandemic. However, the spread of the Delta variant was outbreak first in Vietnam in late April 2021, in which Ho Chi Minh City was the worst affected. This study surveyed the public's knowledge, attitude, perception, and practice (KAPP) toward COVID-19 during the rapid rise course of the outbreak in Ho Chi Minh City. This cross-sectional survey was conducted from 30th September to 16th November 2021, involving 963 residents across the city. We asked residents a series of 21 questions. The response rate was 76.6%. We set a priori level of significance at α = 0.05 for all statistical tests. The residents' KAPP scores were 68.67% ± 17.16, 77.33% ± 18.71, 74.7% ± 26.25, and 72.31% ± 31, respectively. KAPP scores of the medical staff were higher than the non-medical group. Our study showed positive, medium-strong Pearson correlations between knowledge and practice (r = 0.337), attitude and practice (r = 0.405), and perception and practice (r = 0.671; p < 0.05). We found 16 rules to estimate the conditional probabilities among KAPP scores via the association rule mining method. Mainly, 94% confident probability of participants had {Knowledge=Good, Attitude=Good, Perception=Good}, as well as {Practice=Good} (in rule 9 with support of 17.6%). In opposition to around 86% to 90% of the times, participants had levels of {Perception=Fair, Practice=Poor} given with either {Attitude=Fair} or {Knowledge=Fair} (according to rules 1, 2, and rules 15, 16 with a support of 7-8%). In addition to the government's directives and policies, citizens' knowledge, attitude, perception, and practice are considered one of the critical preventive measures during the COVID-19 pandemic. The results affirmed the good internal relationship among K, A, P, and P scores creating a hierarchy of healthcare educational goals and health behavior among residents.

Analysis of Factors Affecting the Over-Representation of Sequential Crashes in Freeway Tunnels: Using Rule-Based Data Mining Method

The paper provides an empirical analysis of road/tunnel design, traffic volume, and environmental factors associated with the increased likelihood of sequential crashes in freeway tunnels. The association rule mining and decision tree methods are employed since both of them are capable of identifying complicated interactions among variables and expressing them in the form of rules. Results show that tunnel length, traffic congestion, time of day, season, and vehicle type are the significant factors influencing the likelihood of sequential crashes in freeway tunnels. More importantly, association rule mining and decision tree analysis reveal that a combination of road/tunnel design, traffic, and environmental factors produces even a higher likelihood of sequential crashes, leading to a series of hazardous situations. For example, when factors including long tunnel and grade ≤ 2%, fourth level, and winter are combined, the proportion of sequential crashes is more than twice the average proportion of sequential crashes in the complete tunnel crash database. Traffic safety management should pay more attention to monitoring these hazardous situations which are more likely to be linked to sequential crashes.