Association Rules Research Articles

Extraction of association rules in a diabetic dataset using parallel FP-growth algorithm under apache spark

This research paper focuses on enhancing the frequent pattern growth (FP-growth) algorithm, an advanced version of the Apriori algorithm, by employing a parallelization approach using the Apache Spark framework. Association rule mining, particularly in healthcare data for predicting and diagnosing diabetes, necessitates the handling of large datasets which traditional methods may not process efficiently. Our method improves the FP-growth algorithm’s scalability and processing efficiency by leveraging the distributed computing capabilities of apache spark. We conducted a comprehensive analysis of diabetes data, focusing on extracting frequent itemsets and association rules to predict diabetes onset. The results demonstrate that our parallelized FP-growth (PFP-growth) algorithm significantly enhances prediction accuracy and processing speed, offering substantial improvements over traditional methods. These findings provide valuable insights into disease progression and management, suggesting a scalable solution for large-scale data environments in healthcare analytics.<p> </p>

Ecological integration of Vespa velutina Lepeletier, 1836 in South Korea: a comprehensive study of co-occurrence and interaction patterns with native social wasps.

The spread of Vespa velutina Lepeletier, 1836 (Asian hornet) in South Korea poses a threat to biodiversity and agriculture. Traditional species interaction models often fail to capture the complexity of invasive species dynamics. This study used association rule learning (ARL) and clustering analyses to explore the interactions of V. velutina with native Vespidae species in South Korea, with the aim of uncovering patterns of coexistence and competition. Over 4 years (2020-2023), 304 traps were strategically placed across South Korea to collect data on Vespidae species. Our findings showed that Vespula flaviceps, Vespa crabro, and Vespula koreensis were the most frequently encountered species. V. velutina was also widespread, suggesting its successful integration into local ecosystems. The ARL analysis, using the 'apriori' algorithm, identified significant co-occurrence patterns and potential interactions. The rules generated indicated both competitive and coexistent relationships with varying levels of association strength across different regions. Clustering analyses, including hierarchical and k-means clustering, grouped species based on their occurrence similarities. The distinct clusters formed in the analysis highlighted the unique ecological roles and interactions of V. velutina and other Vespidae species in South Korean ecosystems. This study confirms the successful establishment of V. velutina in diverse South Korean ecosystems and highlights its complex interactions with native species. These findings support the need for nuanced management strategies that consider the intricate ecological relationships of V. velutina. This approach is crucial for the effective management of invasive species and ecosystem conservation. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Enhancing interpretability in random forest: Leveraging inTrees for association rule extraction insights

The random forest model is a powerful supervised learner, recognized for its ability to learn the pattern within data with superior predictive accuracy. However, it is a black box model because it lacks interpretability. This study addressed the interpretable challenge by employing the inTree framework. The rules were extracted from each decision tree in a random forest model, and the association rules were determined through measured matrix support and confidence to reveal the frequent variable interactions for predicting unemployment. This approach provided insight into the relationships between specific variables and unemployment outcomes. The developed method used data set from the integrated labor force survey (ILFS) 2020/2021 in Zanzibar. Zanzibar’s unemployment rate consistently increased across surveys conducted in 2006, 2014, and 2020/2021. Results have shown that the rules that most predict unemployment for individuals are female and lack of health insurance and secondary education level, female and youth age group and lack of health insurance and secondary education level with a high confidence level. This study provides practical insights for Zanzibar’s government to develop effective interventions, programs, and policies. Improving the interpretability of the random forest model enhances decision-making to address unemployment challenges.

Evaluating the implementation of the Sustainable Sites Initiative (SITES) and its adaptation potential in China

The importance of green infrastructure for achieving urban sustainability is increasingly recognized globally. Originating in the United States, the Sustainable Sites Initiative (SITES) is an international green rating system designed to guide sustainable landscape development and evaluate sustainability throughout the entire lifecycle of landscapes and other outdoor spaces. Despite its international scope, the performance of SITES has not been quantified in diverse contexts. Understanding the performance of SITES is crucial for practitioners and regulatory authorities; however, quantitative analysis of previous projects is lacking. Furthermore, the adaptation of SITES in China requires further exploration. This study addresses these gaps by utilizing a dataset of SITES v2 certified projects to examine the achievement of sections and credits across various projects, aiming to understand performance patterns and identify barriers to certification. Performance was assessed using score ratios to measure how effectively projects met potential credits, and Association Rules Mining was employed to explore relationships between related credits. Furthermore, a detailed case study of Beijing Central Green Forest Park was conducted to assess the adaptability and applicability of SITES within the Chinese context. Our findings indicate significant variability in the difficulty of achieving certification across different sections. Among 1166 potential rules, 128 pairs of related credits were identified, suggesting substantive associations among the credits. The case study of Beijing Central Green Forest Park resulted in an overall score ratio of 82%, achieving 171 scores, which underscores the potential of SITES as a valuable sustainability tool in China. However, variability in section performance, particularly lower scores in the Materials Selection section, highlights the challenges faced and emphasizes the need for further adaptations of the SITES criteria to better align with local conditions.

Online Learning of Temporal Association Rule on Dynamic Multivariate Time Series Data

Online Learning of Temporal Association Rule on Dynamic Multivariate Time Series Data

Exploring the critical factors influencing the severity of maritime accidents via multinomial logit model with adaptive sparse group lasso penalty

Identifying the most influential factors affecting the severity of maritime accidents is crucial for developing effective strategies to reduce accident losses. However, this task is challenging due to the complex interactions between influencing factors and the presence of numerous irrelevant and redundant factors. As a new effort, this paper proposes a multinomial logit model with adaptive sparse group lasso penalty (MLASGL) to identify critical factors influencing the severity of maritime accidents. To fully illustrate the effect of complex interactions on severity, factor importance is initially evaluated using association rule mining and complex networks. Subsequently, adaptive weights are constructed based on the factor importance evaluation, followed by proposing an adaptive sparse group lasso that can impose a greater penalty on the noise factors. Additionally, considering that severity as a decision-making objective is often a multi-categorical variable, the multinomial logit is utilized to portray the relationship between the influencing factors and severity. Simultaneously, the negative log-likelihood function of the multinomial logit model is incorporated into the proposed adaptive sparse group lasso to eliminate irrelevant and redundant factors, thereby realizing the identification of critical factors. Finally, the feasibility and superiority of the proposed method are demonstrated by analyzing a real maritime accident dataset.

Analysis of Network Security Measures for Protecting Organizational Data and Implement Digital Ideas

Objectives: The goal of this study is to proposed analysis of Efficacious Intellectual Framework for Host based IDS improves the quality of the Association Rule Mining. Methods: For these 15 examiners were appropriated. To create great reactions, a solitary survey is utilized to quantify numerous hypothetical builds of the review. The poll is separated into three fundamental segments, where, the main area manages general inquiries. In the subsequent segment, the respondents are mentioned to pick one explicit elective that presents the situation with given secuity practice in their organization. The Herberos convention was utilized in this review. Results: Brute-Force method is used to reduce the total running time of the framework when the proposed framework with suggested IRG method is used for unknown rule generation and single keyword pattern matching. The proposed framework consumes less memory and less execution time. Conclusions: This approach is taken advantage of to increase the expectation precision. In this commitment, a creative Bright Characterization Structure is wanted to uplift the exactness of the grouping in the IDS. The Proposed PFSM technique prompted seize the unimportant component in the IDS dataset for the characterization of the organization. From the result achieved, it is validated that the proposed approach achieved its uniqueness over the normal element determination method executed in the Interruption Discovery Framework. Additionally, it likewise progresses the forecast exactness and reduces the mistake rates. The minimization of blunder rates is the result of incredible order precision.

An Advanced Apriori Algorithm Technology to Enhance Sports Data Mining

This paper explores the optimization of association rule mining in the context of physical education (PE) data, focusing on modifying the Apriori algorithm to enhance its efficiency and accuracy. Recognizing the unique characteristics of PE data—such as the consistent length and limited scope of physical test metrics—the study proposes a novel approach that combines transaction compression with hash technology to refine the Apriori algorithm. This enhanced algorithm aims to analyze the correlations between physical fitness indicators effectively and quickly to identify key factors influencing student performance. Experimental results demonstrate that the improved model significantly increases operational efficiency while maintaining high mining accuracy. Additionally, the research addresses the integration of ecological education within PE, emphasizing the dynamic balance concept of “balance-unbalance-adaptation-balance” in optimizing the educational ecosystem. The findings are helpful for providing practical tools for educators to advance this field.

A cognitive computing approach for product configuration design based on associative rule mining and deep reinforcement learning

Product configuration design is instrumental in combining available configurable components to rapidly generate new products that align with customised requirements, aiding manufacturing enterprises in enhancing production efficiency and reducing development costs. Nevertheless, traditional methods remain inadequate for effectively leveraging historical configuration knowledge and rapidly generating new configuration design schemes devoid of established references. Inspired by the combined effects of the associative learning of the cerebellum and the reward modulation learning of the basal ganglia on goal-oriented behaviours, this paper proposes a cognitive computing method for product configuration design based on associative rule mining and deep reinforcement learning. For one thing, the benchmark learning-based association rule mining method, which simulates the cerebellum's function, automatically extracts configuration design knowledge that connects customer requirements with module instances, thereby promoting the end-to-end reusing of historical configuration design solutions. For another, the improved deep Q-network is proposed to mimic the function of the basal ganglia, which is employed to generate new configuration design schemes in the absence of historical precedents. A case study on the configuration design of the essential service water system of a nuclear power plant is implemented to demonstrate the reliability and reasonability of the proposed approach.

Interval Linguistic-Valued Intuitionistic Fuzzy Concept Lattice and Its Application to Linguistic Association Rule Extraction

Interval Linguistic-Valued Intuitionistic Fuzzy Concept Lattice and Its Application to Linguistic Association Rule Extraction

Leveraging Swarm Intelligence for Invariant Rule Generation and Anomaly Detection in Industrial Control Systems

Industrial control systems (ICSs), which are fundamental to the operation of critical infrastructure, face increasingly sophisticated security threats due to the integration of information and operational technologies. Conventional anomaly detection techniques often lack the ability to provide clear explanations for their detection, and their inherent complexity can impede practical implementation in the resource-constrained environments typical of ICSs. To address these challenges, this paper proposes a novel approach that leverages swarm intelligence algorithms for the extraction of numerical association rules, specifically designed for anomaly detection in ICS. The proposed approach is designed to effectively identify and precisely localize anomalies by analyzing the states of sensors and actuators. Experimental validation using the Secure Water Treatment (SWaT) dataset demonstrates that the proposed approach can detect over 84% of attack instances, with precise anomaly localization achievable by examining as few as two to six sensor or actuator states. This significantly improves the efficiency and accuracy of anomaly detection. Furthermore, since the method is based on the general control dynamics of ICSs, it demonstrates robust generalization, making it applicable across a wide range of industrial control systems.

Design and Implementation of Personalized Recommendation Algorithm in Alumni Using Natural Language Processing

Recently, new possibilities for its web portal application have been presented by the automatic discovery of information in educational data, which has been expanding its horizons. The recommendation of university courses for high school students is an unexplored area. Traditional recommendation systems rely on collaborative filtering, which, in this instance, does not apply since the number of items and users needed to achieve high-performance levels is too small. One of the most well-known recommendation approaches in the course recommendation career is Natural Language Processing (NLP). Association rules have been added using data from alumni and assessments of student recommendations. The rules have been created by first mining data about the course that students completed, then comparing the discovered relationships to the existing course tree to improve it. Hence, a Natural Language Processing-based Personalized Recommendation System (NLP-PRS) has been established to help students choose courses that would take them to their desired careers by analyzing data from alumni and job offers. This study can use the alumni web portal and content formation through NLP to extract relevant information from course programs. It then presents recommendations based on students’ performance, interests, and results in each course that makes up each program. In addition, for the best results in improving their learning skills, students’ decisions should inform the recommended compliance. Because students vary in preferences (based on prior knowledge, learning style, emotional state, etc.), it is impossible to recommend a set timeframe for the recommended sequence. Based on the experimental outcome data, it gives the impression that the system is superior for allowing alumni to engage, share updates, and interact with current students who are alumni.

Music Sequence Creation Technique in Piano Music Production Based on Embedded Neural Network

With the rapid development of computer technology and artificial intelligence, especially the rise of embedded neural network technology. The traditional arrangement methods are facing increasing challenges in the field of music production, and their complexity and time-consuming nature are no longer sufficient to meet the urgent needs of modern audiences for efficient and high-quality computer piano music works. This paper proposes an innovative solution — a piano music production matching method based on embedded neural network fusion, and particularly focused on intelligent creation technology for music sequences. In the system, embedded neural networks are core applied in the process of creating music sequences. By utilizing their powerful feature learning and generalization abilities, neural networks can capture complex patterns and dynamic changes in music sequences, providing infinite possibilities for melody generation and innovation. At the same time, combined with association rule algorithms, the system can discover potential association patterns between notes, further optimizing the fluency and harmony of the melody; The clustering algorithm helps extract common features from massive music data, providing a rich material library for melody innovation.

Enhancing employee performance appraisal through optimized association rule algorithms: a data mining approach

The planning and development department of the China Yangtze river three gorges engineering development corporation aims to improve its intelligent performance management system in accordance with the company’s vision. This paper presents a study on enhancing intelligent performance management in the Planning and Development Department of the China Yangtze River Three Gorges Engineering Development Corporation. By combining data mining techniques with association rule algorithms, a performance prediction model was established. The goal is to explore a performance management system aligned with the company’s vision and the specific characteristics of construction enterprises. Experimental results show the effectiveness of the GABPNN model, achieving a prediction error of 5% or less in seven out of seventeen sample points, with a maximum relative error of 20.86%. The suggested performance management system presents a useful strategy for construction companies to improve their performance evaluation procedures, boosting overall management efficiency and aligning with their distinctive traits.

Analyzing Daytime/Nighttime Pedestrian Crash Patterns in Michigan Using Unsupervised Machine Learning Techniques and their Potential as a Decision-Making Tool

Background Data mining applications are becoming increasingly common across various fields. Numerous data mining methodologies have been utilized in pedestrian crash data analysis. However, association and correspondence analyses have yet to be extensively employed in pedestrian safety literature to support decision-making. Material and Methods Road lighting significantly affects pedestrian crashes, highlighting the importance of examining pedestrian crash patterns during daytime and nighttime. This study analyzed pedestrian fatal and injury crashes in Michigan from 2011 to 2021 under varying road lighting conditions. The study identified pedestrian crash patterns using unsupervised machine-learning techniques based on several multidimensional factors. The Association Rules Learning (ARL) technique was used to determine general associations of patterns leading to pedestrian crashes. At the same time, Multiple Correspondence Analysis (MCA) helped understand crash attribute patterns in two notable scenarios: elderly pedestrian crashes during daytime lighting and high-speed midblock crashes during nighttime. These methods revealed key patterns associated with contributing factors to pedestrian crashes in different lighting conditions. Results In the analysis of cloud combinations involving elderly pedestrians during daylight, it was found that: 1) elderly pedestrians were significantly involved in severe injury crashes at two-lane midblock locations with a speed limit between 45 and 65 mph; 2) improper actions by elderly drivers were significantly associated with elderly pedestrians on wide roads (>5 lanes); and 3) the complex interaction between city streets with 3-5 lanes was significantly correlated with drivers 'failed to yield' actions. Additionally, in the analysis of cloud combinations involving nighttime pedestrian crashes at high-speed midblock locations, it was found that 1) a specific age group of alcohol- or drug-involved pedestrians (19-30) was significantly associated with young drivers and improper driver actions; 2) during the winter season, young pedestrians were significantly involved in moderate injury crashes at undivided midblock locations; and 3) elderly drivers and 'failed to yield' actions were highly correlated with 3-5 lane roadways. Discussion The research findings are expected to raise awareness of Michigan pedestrian crash patterns under daytime and nighttime lighting conditions. They will also recommend safety countermeasures for practitioners to enhance pedestrian safety in walkable cities. The proposed methodologies can uncover relationships that need to be well-documented in the existing literature on pedestrian safety. Additionally, these methods can identify valuable relationships without restricting variables to being either dependent or independent, and they can reveal relationships that might be challenging to detect otherwise. The results will also provide decision rules and visualizations that are easy to understand. Conclusion The results showed that the proposed techniques can analyze pedestrian crash data in a way that aligns with understanding pedestrian crash characteristics. Traffic safety administrators could benefit from this methodology as a decision-support tool.

Identifying attribute associations in fatal speeding crashes using latent class clustering and association rule mining

Speeding has been distinguished as one of the most frequent and persistent contributing factors and is a critical contributing factor to the degree of injury severity. In the United States, at least a quarter of nationwide annual fatal crashes during the last decade involved speeding. There is still a need for an overarching look at crashes involving speeding by considering a wider set of crashes, roadway, driver, and vehicle characteristics. Despite extensive research on speeding-related crashes, there is limited understanding of how collective variables in homogeneous crash clusters contribute to fatal speeding crashes. This paper addresses this gap by investigating these collective impacts using fatal crash data from the Fatality Analysis Reporting System (FARS). Using crash data from the 2015–2019 FARS repository, this study applies latent class clustering (LCC) to obtain homogeneous clusters of fatal speeding crashes, addressing the unobserved heterogeneity. Association rule mining (ARM) has been applied to homogeneous clusters to find hidden patterns. The finding of association rules, such as motorcycle speeding, single vehicle crashes during weekends, in dark, unlit conditions, etc. The results of this research and interpretative findings are expected to improve the knowledge of speeding-related crash mechanisms and to provide important insights on countermeasure development.

Analyzing Autonomous Vehicle Collision Types to Support Sustainable Transportation Systems: A Machine Learning and Association Rules Approach

The increasing presence of autonomous vehicles (AVs) in transportation, driven by advances in AI and robotics, requires a strong focus on safety in mixed-traffic environments to promote sustainable transportation systems. This study analyzes AV crashes in California using advanced machine learning to identify patterns among various crash factors. The main objective is to explore AV crash mechanisms by extracting association rules and developing a decision tree model to understand interactions between pre-crash conditions, driving states, crash types, severity, locations, and other variables. A multi-faceted approach, including statistical analysis, data mining, and machine learning, was used to model crash types. The SMOTE method addressed data imbalance, with models like CART, Apriori, RF, XGB, SHAP, and Pearson’s test applied for analysis. Findings reveal that rear-end crashes are the most common, making up over 50% of incidents. Side crashes at night are also frequent, while angular and head-on crashes tend to be more severe. The study identifies high-risk locations, such as complex unsignalized intersections, and highlights the need for improved AV sensor technology, AV–infrastructure coordination, and driver training. Technological advancements like V2V and V2I communication are suggested to significantly reduce the number and severity of specific types of crashes, thereby enhancing the overall safety and sustainability of transportation systems.

Optimized evaluation of the quality of sensor video internet of things (VIOT) by the integration of big data and artificial intelligence

The application of sensor video internet of things technology to large-scale integrated work can significantly improve the working quality of employees. However, the degree of improvement in working quality is still difficult to measure in a systematic, intelligent, stable, and accurate manner. local optimization and adjustment after evaluation are still relatively challenging, To address these issues, the study proposes a method of optimizing the evaluation of sensor video quality through the integration of big data and AI techniques. A large-scale integrated distance education system in the field of education and training with a certain application basis is adopted as a case. Including big data and AI techniques such as integrated intelligent agent modules, recommendation algorithms, and transaction optimization algorithms, a new agent-oriented system design with fast response speed, strong scalability, convenient local optimization, and greater stability is achieved. According to the network topology structure of the distance education system in colleges and universities, this paper uses queuing theory to analyze the system performance of the system. The focus of this paper is the quantitative relationship between system communication intensity ρ, user arrival rate λ, system channel capacity n and system waiting delay, blocking probability, average queue length, system throughput and other important performance indicators. In teaching evaluation, the key factor that affects the quality of classroom teaching, that is, Developing a comprehensive system for evaluating classroom instruction is crucial. By incorporating student feedback, leveraging data mining techniques, and harnessing computer technology, a holistic framework for gathering, analyzing, and generating actionable insights on teaching performance is established. This approach makes the evaluation process more systematic and evidence-based, identifying 12 key elements that influence classroom education standards. In the experimental section, the student assessment data sets I1 and I2 exhibit experimental values (statistics) that significantly exceed the thresholds, with a minimum support of 0.32 and a confidence level of 0.61. Moreover, the Boolean matrix is divided into 90 points. The rule U1Ua ≥ U2 is identified as a subset of {U1U2Ua} within the large item set, signifying a strong association rule. These findings confirm the robustness of the artificial intelligence model proposed in this paper for video quality prediction. The optimized sensor video quality evaluation method not only meets a satisfactory confidence level and matching value but also demonstrates good reliability and relevance in the evaluation criteria.

Mining text for causality: a new perspective on food safety crisis management

The aim of the present study was to quantitatively analyze the importance of each risk factor in a food safety event, so as to fully elucidate the correlation between different risk factors and provide a reference for food safety governance. Text mining and complex network analysis methods were utilized to explore the causal mechanism of food safety incidents. By performing text mining on food safety event news reports, 15 major risk factors were identified based on high-frequency words. A causal network for food safety accidents was then constructed using strong association rules among these factors. Through network centrality analysis, the five core factors of food safety incidents and their associated sets were clarified. Based on text mining of 6,282 cases of food safety incidents reported by online media, 168 keywords related to food risk factors were extracted and further categorized into 15 types of food safety risk factors. Network analysis results revealed that microbial infection emerged as the most critical risk factor, with its associated sets including biotoxins and parasites, counterfeiting or fraud, processing process issues, and non-compliance with quality indicators.

Association Rules Analysis for Continuous Chicken Egg Traits Dataset

This study introduces the association rule algorithm Apriori on a dataset consists of 14 continuous egg quality traits recorded from 4320 eggs of three commercial white-laying chicken lines. In the study all the continuous data were discretized using Equal-Width-Interval method based the number of intervals obtained with Rice formula. Association rules analysis on the discretized dataset resulted with a total of 349 rules consists of 3 and 4 items. According to the top five rules by support and confidence, some important associations were obtained between the certain value ranges of the traits egg weight, egg width, egg length, shell thickness, and shell breaking strength when compared to the others. The appropriate biological and economic interpretations of the obtained rules may contribute to the poultry industry in practice.