Association Rule Mining Research Articles

Course Evaluation and Improvement Based on Association Rule Mining in English Online Teaching and Learning

Association rule mining in English online teaching and learning involves the analysis of patterns and relationships within large sets of data to uncover meaningful insights that can enhance the teaching and learning experience. By examining student interactions, performance data, and usage patterns on online learning platforms, educators can identify correlations between different learning activities, student characteristics, and learning outcomes. This paper explores the application of the Frequent Pattern System Modelling Association Rule (FPSMAR) in the realm of online English teaching and learning. FPSMAR offers a data-driven approach to analyzing student interaction data, uncovering meaningful patterns and associations between different elements of the online learning environment and student outcomes. Through the analysis of association rules and frequent patterns, educators gain valuable insights into effective instructional strategies, learning patterns, and areas for improvement. The study presents association rules indicating relationships such as the correlation between multimedia resource usage and high language proficiency or active online discussion participation and enhanced speaking ability. The study reveals association rules indicating strong relationships, such as a correlation coefficient of 0.80 between multimedia resource usage and high language proficiency, or a confidence level of 0.85 indicating enhanced speaking ability through active online discussion participation.

Design and Implementation of Teaching Quality Assessment System for Universities Based on Data Mining Algorithms

A teaching quality assessment system for universities based on data mining algorithms utilizes advanced analytical techniques to evaluate and enhance the effectiveness of teaching practices. By leveraging data mining algorithms, such as clustering, classification, and association rule mining, this system can analyze various educational data sources, including student performance metrics, course evaluations, and instructor feedback. This paper introduces Generative Pre-trained Data Analytics (GPDA) as a novel approach for assessing teaching quality in educational settings. GPDA leverages advanced data analytics techniques to predict teaching quality metrics, classify teaching quality data, and forecast various aspects of teaching effectiveness. Through comprehensive analysis and evaluation, GPDA demonstrates its accuracy, reliability, and versatility in capturing the complexities of teaching quality across diverse educational contexts. Through comprehensive analysis and evaluation, GPDA achieves a Root Mean Squared Error (RMSE) ranging from 0.152 to 0.190 and R-squared (R²) values ranging from 0.698 to 0.805, showcasing its accuracy and reliability in predicting teaching quality metrics across diverse educational contexts. Furthermore, strong positive correlations between the synthetic data generated by GPDA and real teaching quality data, with correlation coefficient values ranging from 0.87 to 0.92, validate the fidelity of GPDA-generated synthetic data.

Healthcare insurance fraud detection using data mining

BackgroundHealthcare programs and insurance initiatives play a crucial role in ensuring that people have access to medical care. There are many benefits of healthcare insurance programs but fraud in healthcare continues to be a significant challenge in the insurance industry. Healthcare insurance fraud detection faces challenges from evolving and sophisticated fraud schemes that adapt to detection methods. Analyzing extensive healthcare data is hindered by complexity, data quality issues, and the need for real-time detection, while privacy concerns and false positives pose additional hurdles. The lack of standardization in coding and limited resources further complicate efforts to address fraudulent activities effectively.MethodolgyIn this study, a fraud detection methodology is presented that utilizes association rule mining augmented with unsupervised learning techniques to detect healthcare insurance fraud. Dataset from the Centres for Medicare and Medicaid Services (CMS) 2008-2010 DE-SynPUF is used for analysis. The proposed methodology works in two stages. First, association rule mining is used to extract frequent rules from the transactions based on patient, service and service provider features. Second, the extracted rules are passed to unsupervised classifiers, such as IF, CBLOF, ECOD, and OCSVM, to identify fraudulent activity.ResultsDescriptive analysis shows patterns and trends in the data revealing interesting relationship among diagnosis codes, procedure codes and the physicians. The baseline anomaly detection algorithms generated results in 902.24 seconds. Another experiment retrieved frequent rules using association rule mining with apriori algorithm combined with unsupervised techniques in 868.18 seconds. The silhouette scoring method calculated the efficacy of four different anomaly detection techniques showing CBLOF with highest score of 0.114 followed by isolation forest with the score of 0.103. The ECOD and OCSVM techniques have lower scores of 0.063 and 0.060, respectively.ConclusionThe proposed methodology enhances healthcare insurance fraud detection by using association rule mining for pattern discovery and unsupervised classifiers for effective anomaly detection.

Survey on A Smart Medical System on Drug Analysis using ML Algorithms

Online health communities continue to offer huge variety of medical information useful for medical practitioners, system administrators and patients alike. In this system we collect real time health posts from reputed websites, where patients express their views, including their experiences and side-effects on drugs used by them. We propose to perform Summarization of user posts per drug, and come out with useful conclusions for medical fraternity as well as patient community at a glance. Further, we propose to classify the users based on their ‘emotional state of mind’. Also, we shall perform knowledge discovery from user posts, whereby useful ‘patterns’ about the triad ‘drugs-symptoms-medicine’ is done by Association Rule Mining. Association rule mining is a popular and widely-known machine learning task. It is used to find out interesting relations between variables in large database. Rules generated by association have two disjoint set of items having form LHS (Left Hand Side) => RHS (Right Hand Side). The rule says that RHS is likely to occur whenever the LHS set occurs.

Selection of ionic liquid electrolytes for high-performing lithium-sulfur batteries: An experiment-guided high-throughput machine learning analysis

The polysulfide (PS) shuttle mechanism (PSM) is one of the most significant challenges of lithium-sulfur (Li-S) batteries in achieving high capacity and cyclability. One way to minimize the shuttle effect is to limit the PS solubilities in the battery electrolyte. Ionic liquids (IL) are particularly suited as electrolyte solvents because of their tunable physical and chemical properties. In this work, thousands of ILs are screened to narrow down potentially viable candidates to be used as electrolytes in Li-S batteries. To that end, the COnductor-like Screening Model for Realistic Solvents (COSMO-RS) calculations are performed over more than 36,000 ILs. An extensive database containing PS solubilities and other relevant properties is constructed at 25 °C. First, the effectiveness of the COSMO-RS calculations is experimentally tested with six different ILs having a wide range of solubility and viscosity values; a strong correlation between the PS solubility and battery performance is obtained. After specifying the target limits for promising ILs using the experimental battery performance data, machine learning (ML) tools are used to predict and identify the relationship between IL properties and PS solubilities and structural and molecular descriptors of ILs. The extreme gradient boosting (XGBoost) method successfully predicts the solubility and property values. Association rule mining (ARM) and the feature importance analysis show that anion descriptors are more dominant, whereas cations have less impact on the solubilities and properties of ILs. Finally, the imidazolium and pyridinium ILs with bis_imide and borate anion groups are identified as the most promising ones.

Deciphering Patterns in a small scale Case Analysis Study of the Apriori Algorithm in Market Basket Analysis using machine learning tools

Analysis of Transaction Database Using Apriori Algorithm. This study examines the use of the Apriori algorithm for analysing transaction databases. The Apriori algorithm is a fundamental technique in data mining that allows for the efficient discovery of frequent patterns and association rules in large datasets. The Apriori algorithm employs a two-step approach. Initially, it identifies frequent items in the database based on a user-defined minimum support threshold. Subsequently, it generates association rules that describe relationships between these frequent items based on metrics such as confidence and lift. This paper provides an in-depth explanation of the Apriori algorithm, emphasizing its strengths and limitations. Additionally, it presents various applications of the Apriori algorithm in real-world scenarios, such as shopping cart analysis, cross-selling and upselling, and customer segmentation. The significance of this study lies in its comprehensive analysis of the Apriori algorithm and its practical relevance in diverse data mining tasks. It serves as a valuable resource for researchers, practitioners, and anyone seeking to understand the fundamentals and applications of association rule mining. Keywords: Datamining, OLTP, Apriori algorithm, Item sets and Market Basket Analysis

Employing supervised machine learning algorithms for classification and prediction of anemia among youth girls in Ethiopia

In developing countries, one-quarter of young women have suffered from anemia. However, the available studies in Ethiopia have been usually used the traditional stastical methods. Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of anemia among youth girls in Ethiopia. A total of 5642 weighted samples of young girls from the 2016 Ethiopian Demographic and Health Survey dataset were utilized. The data underwent preprocessing, with 80% of the observations used for training the model and 20% for testing. Eight machine learning algorithms were employed to build and compare models. The model performance was assessed using evaluation metrics in Python software. Various data balancing techniques were applied, and the Boruta algorithm was used to select the most relevant features. Besides, association rule mining was conducted using the Apriori algorithm in R software. The random forest classifier with an AUC value of 82% outperformed in predicting anemia among all the tested classifiers. Region, poor wealth index, no formal education, unimproved toilet facility, rural residence, not used contraceptive method, religion, age, no media exposure, occupation, and having more than 5 family size were the top attributes to predict anemia. Association rule mining was identified the top seven best rules that most frequently associated with anemia. The random forest classifier is the best for predicting anemia. Therefore, making it potentially valuable as decision-support tools for the relevant stakeholders and giving emphasis for the identified predictors could be an important intervention to halt anemia among youth girls.

Analysis of the characteristics and causes of city gas explosion accidents based on DT-AR algorithm

In order to effectively prevent and control accidents, it is essential to trace back the causes of gas explosions in cities. The DT-AR(decision tree-association rule) algorithm is proposed as a quantitative analysis of gas accident features and causality. First, 210 gas explosion accident investigation reports were taken as samples. The gas accident causation system is divided into three aspects, including environmental factors, management factors and physical factors. Management factors were sorted into organizational-level and individual-level factors from the investigation reports. Second, the CART decision tree model was used to compare location features, organizational causality features, and individual causality features of the piped and bottled gas accidents, and a decision tree model with the gas system fault site as the root node was built to filter the key feature variables. In order to reveal factor correlations and deep-level causation, the Apriori algorithm is used to mine accident association rules. The combinations on the branches of the decision tree are used as constraints to filter the critical causality rule, which improves the efficiency of association rule screening and enhances prediction accuracy. The results demonstrate that the DT-AR algorithm can evaluate the importance of variables, quickly locate effective combinations of factors, and mine the complete causal chain. The association rule is screened based on the constraint of the key element combination of the decision tree, which compensates for the low efficiency of the Apriori algorithm for association rule mining. In addition, the accident-caused excavation results provide an effective path for gas companies, outsourced service companies and administrative departments to implement gas safety chain supervision, which can address the problem of gas accident safety management failures and provide decision support for accident prevention.

An ensemble maximal feature subset selection for smartphone based human activity recognition

Smartphone-based Human Activity Recognition (HAR) uses spatiotemporal time series data collected from a smartphone’s in-built accelerometer, gyroscope, and magnetometer sensor. Real-time HAR datasets such as UCI-HAR and UCI-HAPT extract time and frequency domain statistical features for activity recognition from the collected time series signals. Various Machine Learning techniques have been proposed in the perspective of feature selection through ensemble, traditional, hybrid, and meta-heuristic-based techniques to improve the recognition performance. Though the feature selection techniques have shown promising results, the computational time and selection of valid significant features for better classification performance still need to be included. Moreover, these techniques are highly parameter-dependent and critical to threshold values used for experimentation. This research proposes a novel method of parameter-free ensemble feature selection by combining traditional feature selection algorithms with the concept of association rule mining to obtain the Maximal Feature Subset (MFS), which is used for training and testing the classification model for HAR. Mathematical association rule mining is framed rather than the traditional one to select the associated frequent feature(s) from the feature set evolved by traditional techniques. Experimentation has been carried out on UCI-HAR and UCI-HAPT, and the proposed method has increased the classification efficiency by selecting significant features and reduces computation time significantly.

Evaluation of Energy Utilization Efficiency and Optimal Energy Matching Model of EAF Steelmaking Based on Association Rule Mining

In the iron and steel industry, evaluating the energy utilization efficiency (EUE) and determining the optimal energy matching mode play an important role in addressing increasing energy depletion and environmental problems. Electric Arc Furnace (EAF) steelmaking is a typical short crude steel production route, which is characterized by an energy-intensive fast smelting rhythm and diversified raw charge structure. In this paper, the energy model of the EAF steelmaking process is established to conduct an energy analysis and EUE evaluation. An association rule mining (ARM) strategy for guiding the EAF production process based on data cleaning, feature selection, and an association rule (AR) algorithm was proposed, and the effectiveness of this strategy was verified. The unsupervised algorithm Auto-Encoder (AE) was adopted to detect and eliminate abnormal data, complete data cleaning, and ensure data quality and accuracy. The AE model performs best when the number of nodes in the hidden layer is 18. The feature selection determines 10 factors such as the hot metal (HM) ratio and HM temperature as important data features to simplify the model structure. According to different ratios and temperatures of the HM, combined with k-means clustering and an AR algorithm, the optimal operation process for the EUE in the EAF steelmaking under different smelting modes is proposed. The results indicated that under the conditions of a low HM ratio and low HM temperature, the EUE is best when the power consumption in the second stage ranges between 4853 kWh and 7520 kWh, the oxygen consumption in the second stage ranges between 1816 m3 and 1961 m3, and the natural gas consumption ranges between 156 m3 and 196 m3. Conversely, under the conditions of a high HM ratio and high HM temperature, the EUE tends to decrease, and the EUE is best when the furnace wall oxygen consumption ranges between 4732 m3 and 5670 m3, and the oxygen consumption in the second stage ranges between 1561 m3 and 1871 m3. By comparison, under different smelting modes, the smelting scheme obtained by the ARM has an obvious effect on the improvement of the EUE. With a high EUE, the improvement of the A2B1 smelting mode is the most obvious, from 24.7% to 53%. This study is expected to provide technical ideas for energy conservation and emission reduction in the EAF steelmaking process in the future.

Influence of emotions on the aggressive driving behavior of online ­car-hailing drivers based on association rule mining

Emotion is an important factor that can lead to the occurrence of aggressive driving. This paper proposes an association rule mining-based method for analysing contributing factors associated with aggressive driving behaviour among online car-hailing drivers. We collected drivers’ emotion data in real time in a natural driving setting. The findings show that 29 of the top 50 association rules for aggressive driving are related to emotions, revealing a strong relationship between driver emotions and aggressive driving behaviour. The emotions of anger, surprised, happy and disgusted are frequently associated with aggressive driving behaviour. Negative emotions combined with other factors (for example, driving at high speeds and high acceleration rates and with no passengers in the vehicle) are more likely to lead to aggressive driving behaviour than negative emotions alone. The results of this study provide practical implications for the supervision and training of car-hailing drivers.

Blockchain Enhanced Student Physical Performance Analysis using Machine Learning-IoT and Apriori Algorithm in Physical Education Network Teaching

In the digital era, particularly with the rise of online teaching, traditional approaches to college physical education face challenges in adequately monitoring and enhancing students’ physical fitness. This study introduces a novel approach that integrates blockchain technology with a Machine Learning-IoT framework to evaluate and improve students' physical performance. Utilizing the Apriori algorithm, enhanced with particle swarm optimization and an improved K-means methodology, this system offers a robust tool for correlating student behavior with sports performance in a secure and decentralized manner. The proposed system uses blockchain for safe data management and IoT for real-time data collection, ensuring privacy as well as efficiency. The algorithm's accuracy, recall, and F1 values on the Iris dataset are 0.947, 0.931, and 0.928, respectively, with a considerable Calinski Harabasz score of more than 240. When applied to university student behavior data, the blockchain-enhanced system successfully mined association rules with a maximum confidence level of 0.923.

Association rule mining algorithm implementation for e-commerce in the retail sector

The growth of online trading platforms and the development of market technology have forced businesses to take part in the analysis of client behavior. Therefore, this research aims to analyze customer behavior in the Kyrgyz Republic to enhance supplier's revenue, service quality, and customer satisfaction. This data was analyzed using the apriori algorithm. Results generated 118 rules which revealed strong connections between items and showed up to 61.06% relationship between the consumption of products, suggesting a connection among the considered items. Thus, the association rule highlights the significance of association rule mining in uncovering valuable insights within sales transaction data. These insights can inform targeted marketing efforts, inventory management, and the enhancement of customer experiences and optimize business strategies to meet customer preferences, ultimately fostering growth and competitiveness in the retail sector.

Weighted Bayesian Belief Network for diabetics: a predictive model.

Diabetes is an enduring metabolic condition identified by heightened blood sugar levels stemming from insufficient production of insulin or ineffective utilization of insulin within the body. India is commonly labeled as the "diabetes capital of the world" owing to the widespread prevalence of this condition. To the best of the authors' last knowledge updated on September 2021, approximately 77 million adults in India were reported to be affected by diabetes, reported by the International Diabetes Federation. Owing to the concealed early symptoms, numerous diabetic patients go undiagnosed, leading to delayed treatment. While Computational Intelligence approaches have been utilized to improve the prediction rate, a significant portion of these methods lacks interpretability, primarily due to their inherent black box nature. Rule extraction is frequently utilized to elucidate the opaque nature inherent in machine learning algorithms. Moreover, to resolve the black box nature, a method for extracting strong rules based on Weighted Bayesian Association Rule Mining is used so that the extracted rules to diagnose any disease such as diabetes can be very transparent and easily analyzed by the clinical experts, enhancing the interpretability. The WBBN model is constructed utilizing the UCI machine learning repository, demonstrating a performance accuracy of 95.8%.

A novel algorithm weighting different importance of classes in enhanced association rules

Importance of methods for explainable AI has been ever growing within many processes; a simple form of knowledge is needed by humans who operate the processes. One of the methods suitable for explainable AI is represented by enhanced association rules on categorical data. This method admits an immediate interpretation. A problem arises when we are interested in finding circumstances for a rare category. Even when we find such a category (e.g., in a subset where this category appears five times more), the overall share of this category may be too small for using this rule within processes (e.g., sending someone to an expensive examination, performing calls to clients), and may therefore be refused by process executors (doctors, salesmen). That is why we have created a new algorithm based on association rule mining called UIC-Miner (UIC stands for Unequal Importance of Categories), which overcomes this problem. This algorithm has a pilot implementation for the purposes of testing and reproducing. The algorithm itself uses categories weighted by their levels of importance. This paper first describes a typical situation when category weights are useful. Then, the new algorithm is described, and several examples of its applications are presented. Finally, possible applications of the available tools are discussed.

Establishment of Safety Management Measures for Major Construction Workers through the Association Rule Mining Analysis of the Data on Construction Accidents in Korea

Despite increasing industrial advancements, fatal and severe accidents, such as “Falls”, “Struck-by”, “Hit by an object”, “Be crushed”, and “Caught-in/between” accidents, persist in developed countries, including Korea. Various methods, including risk assessment, monitoring systems, technology improvements, and safety education, are being implemented to reduce accidents. However, only a few studies have revealed the causes of accidents and their interrelationships; these studies are based on limited data. Korea recently published accident data using national statistical systems, including the construction safety management integrated information (CSI), enabling the analyses of major accident types. Here, we selected various representative accident cases to minimize the duplication of the data published from 2019 to 2023 and applied the Material, Method, Machine, or Man (4M) analysis method, a risk assessment technique, to perform an accident-type-based association rule mining (ARM) analysis of the accident factors. Through the ARM analysis, we quantitatively identified complex correlations for major accidents. Based on the 4M factors derived through this analysis, we improved a 2–4 model for accident causation and proposed safety management measures for each construction entity.

Enhancing Multi-Channel Consumer Behavior Analysis: A Data-Driven Approach using the Optimized Apriori Algorithm

Purpose –In the dynamic arena of retail and e-commerce, understanding customer behavior is crucial. This research paper investigates the strategies customers use to balance convenience and cost. It notes that consumers often divide their purchases, choosing online platforms for items they perceive as reasonably priced, while preferring physical stores for products associated with high shipping costs, lack of express shipping, or higher online prices. Traditional retail analysis methods, such as identifying frequently purchased and co-occurring items, are critical to the sector. However, these insights may be skewed if the analysis is solely confined to the online shopping cart, neglecting related purchases made at physical stores either before or after the online transaction. This paper addresses this analytical gap by looking into the complex interplay between online and offline purchases. The objective is to provide a more comprehensive, detailed understanding of multi-channel shopping behaviors, allowing retailers to better cater to their customers' needs and preferences while optimizing their strategies for a more effective market presence. This comprehensive approach attempts to uncover deeper insights into the interactions between online and offline purchases, thereby contributing to a more comprehensive understanding of consumer behavior in the retail and e-commerce industries.
 Design/methodology/approach- A detailed survey questionnaire was administered, obtaining 2465 responses. The questionnaire was specifically designed to capture the complexities of online and offline purchases, with an emphasis on Apple's purchase channels. The Apriori algorithm was used twice to find frequently co-occurring items that were purchased online and then online clubbed with offline. Essential metrics like support and confidence were calculated for both online and offline purchases. To determine any significant differences between the groups, an independent-samples t-test was used. This comprehensive methodology ensures a thorough examination of multi-channel shopping behaviors, providing valuable insights into consumer behavior in the retail and e-commerce sectors.
 Findings–We find that cross channel switching in a multi-channel distribution environment occurs due to price differences, convenience and flexibility. The paper illustrates the flaws of drawing inferences with data from a single channel emphasizing the need for a more holistic, multi-channel approach to data analysis in order to capture the full range of consumer behavior.
 Research limitations/implications –
 This study assumes that self-reported purchases or acquisitions of products accurately reflect actual consumer behavior. The insights derived from this research have significant implications for various marketing strategies, including pricing, product bundling, cross-selling, and promotional activities. However, it’s important to note that the reliance on self-reported data may introduce certain limitations, as it may not fully capture the complexity of actual consumer behavior. Despite these limitations, the insights gained from the study will add value for several marketing decisions like pricing, bundling, cross-selling and promotions.
 Originality value– While there is an abundance of literature on the application of Association Rule Mining for Market Basket Analysis, the existing body of work exploring its application within a multi-channel context is notably sparse. This study aims to address this gap in the literature, offering a unique perspective and making a substantial contribution to the existing body of knowledge in this area.

Risk assessment of construction safety accidents based on association rule mining and Bayesian network

Risk assessment of construction safety accidents based on association rule mining and Bayesian network

A Binary Grey Wolf Optimizer with Mutation for Mining Association Rules

A Binary Grey Wolf Optimizer with Mutation for Mining Association Rules

C3PW: A Novel Machine Learning Method for Assessing Percutaneous Transvenous Mitral Commissurotomy Outcome in Patients with Mitral Stenosis

Background: Patients with symptomatic moderate-to-severe mitral stenosis (MS) with pliable valves are indicated for percutaneous transvenous mitral commissurotomy (PTMC) typically over a valve replacement based on favorable anatomic characteristics. Currently, this decision is arrived based on Wilkins’ echocardiographic score which is considered to be a simplistic estimate often. In the current work, the authors present a novel machine learning (ML) framework that considers a comprehensive set of clinical and echocardiographic variables to indicate the possible outcome for choosing the transvenous procedure over an invasive surgery. Methods: Data considered for this retrospective study included demographic, clinical, and preprocedural echocardiographic variables pertaining to patients with moderate-to-severe MS without significant mitral regurgitation (MR). The success of the procedure was defined by four different postprocedural variables such as the final mitral valve area (MVA), MR, left atrial pressure, and right ventricular systolic pressure (RVSP). Three data mining tasks highlighting the importance of ML techniques to predict the outcome of the PTMC procedure in patients with MS have been demonstrated. The potential predictive value of the outcome of a PTMC procedure considering a comprehensive set of variables using ML techniques has not been investigated till date according to the authors’ knowledge. Cover coefficient-based clustering power as weights (C3PW), a novel problem transformation technique which deals with the multilabel classes, is proposed to correctly classify the patients having successful PTMC procedure. Results: Extreme gradient boosting (XGB), an ML technique, gave the best performance (accuracy: 0.79; F-score: 0.87) on the transformed single-label problem. Application of association rule mining revealed that a combination of the following parameters such as “initial MVA” <1 cm2, “RVSP” <50 mmHg, “valvular calcification” score ≤2, “leaflet mobility” score ≤2, and “normal sinus rhythm” plays a crucial role in determining the success of the PTMC procedure. The identified variables with their indicated ranges having a significant “I-rule” value can be put together as a scoring index to predict the successful outcome. Conclusions: A ML-based artificial intelligence tool has been demonstrated to serve as an alternative to the Wilkins score to select patients for a successful PTMC procedure. Of the various models tested, the C3PW approach with XGB algorithm demonstrated the best evaluation metrics. A systematically analyzed ML framework that yields highly interpretable and conclusive findings with high confidence has been demonstrated to be a useful tool in clinical decision-making.