Association Rule Mining Algorithm Research Articles

Big data technology for teaching quality monitoring and improvement in higher education - joint K-means clustering algorithm and Apriori algorithm

With the development of big data technology, the field of monitoring and improving teaching quality in universities has ushered in new opportunities and challenges. Big data technology enables the capture and analysis of massive amounts of data generated during the teaching process, providing the possibility for a deeper understanding of teaching activities. However, how to extract useful information from these vast amounts of data and transform it into strategies for teaching improvement is a challenge. The research aims to propose a teaching quality monitoring and improvement method based on big data technology, which combines K-means clustering algorithm and association rule mining algorithm to improve the accuracy of teaching monitoring and the effectiveness of teaching improvement. In order to cope with these challenges, the study proposes a research method of big data technology based on joint K-mean clustering algorithm and association rule mining algorithm. The study first analyzes the teaching quality monitoring and evaluation indexes using the K-mean algorithm. Then the association rule mining algorithm is utilized to mine the data in the teaching quality monitoring indicators with association rules on the basis of the obtained cluster analysis. Finally, on the basis of association rule mining, the study constructs the assessment model of teaching quality monitoring indicators by utilizing the fused method. The outcomes revealed that the average of data analysis accuracy and the average of recall rate of the modeling method were 93.79 % and 91.95 %, respectively. Meanwhile, the evaluation time of the modeling method in the process of teaching quality monitoring data processing was 17.3 s, and the evaluation precision was 93.15 % respectively. Additionally, the process's overall confidence and enhancement are 95.01 % and 86.73 %, respectively, and the modeling method's performance is compared to other approaches. This shown that the approach may significantly boost the precision and effectiveness of monitoring the quality of instruction, as well as offer strong backing for the enhancement of instruction in higher education institutions.

English Network Teaching Model and Design of Evaluation System Based on Association Rule Algorithm

This study innovates English network teaching by applying a refined Association Rule Mining (ARM) algorithm. It integrates an “interest” parameter into ARM, dynamically adapting content to individual learners' profiles, improving engagement and outcomes. Controlled experiments, spanning diverse online platforms, validate the ARM model's efficacy by correlating learning content with academic performance, specifically CET-4 and CET-6 scores. Comprehensive preprocessing ensures data quality and privacy, employing techniques like de-identification, data perturbation, and aggregation. Advanced data analysis, including cross-validation and multivariate techniques, bolsters findings' reliability. Results highlight the ARM model's capacity to generate personalized learning paths, transcending conventional methods, and its potential as a cornerstone for data-driven education reforms. Future research will explore machine learning refinements and cultural adaptability to broaden its impact, fostering equitable, high-quality digital English education worldwide.

Identifying core IoT technologies using ARM and FCM: A comprehensive data-driven method

The internet of things (IoT) technology has garnered significant attention in recent years due to its wide-ranging applications. IoT, with its high connectivity capabilities, integrates various industrial, domestic, and agricultural devices into a smart and remotely controllable software and hardware platform. The field of IoT technology is expansive and encompasses a multitude of sub-technologies. Identifying core technologies in this domain is crucial for guiding research and development efforts by companies. Given the interrelation of these core technologies and their combination with recent decision-making approaches, network-based strategies have recently received special attention. The developed methods are based on static conditions and the assumption of stability, while in emerging technologies like IoT, the pace of changes over time is high. This leads to changes in the importance of technologies under various scenarios.In this study, in order to analyze the extracted patent data, association rule mining (ARM) algorithms were applied to identify the relationships between technologies and social network analysis was used to analyze the relationships between technologies and estimate their initial weights. Finally, fuzzy cognitive map (FCM) were used to estimate the final weights of technologies and rank them. The fcm approach allows for simultaneous modeling of both static and dynamic states of the system and, on the other hand, by calculating under various scenarios, suggests a core technology that is sustainable.The research results show that digital information transmission technologies, digital or electrical data processing, and wireless communication networks are the most important sub-technologies of Internet of things.

Software Fault Localization Based on Weighted Association Rule Mining and Complex Networks

Software fault localization technology aims to identify suspicious statements that cause software failures, which is crucial for ensuring software quality. Spectrum-based software fault location (SBFL) technology calculates the suspiciousness of each statement by analyzing the correlation between statement coverage information and execution results in test cases. SBFL has attracted increasing attention from scholars due to its high efficiency and scalability. However, existing SBFL studies have shown that a large number of statements share the same suspiciousness, which hinders software debuggers from quickly identifying the location of faulty statements. To address this challenge, we propose an SBFL model based on weighted association rule mining and complex networks: FL-WARMCN. The algorithm first uses Jaccard to measure the distance between passing and failing test cases, and applies it as the weight of passing test cases. Next, FL-WARMCN calculates the initial suspiciousness of each statement based on the program spectrum data. Then, the FL-WARMCN model utilizes a weighted association rule mining algorithm to obtain the correlation relationships between statements and models the network based on this. In the network, the suspiciousness of statements is used as node weights, and the correlation between statements is used as edge weights. We chose the eigenvector centrality that takes into account the degree centrality of statements and the importance of neighboring statements to calculate the importance of each statement, and used it as a weight to incorporate into the weighted suspiciousness calculation of the statement. Finally, we applied the FL-WARMCN model for experimental validation on the Defects4J dataset. The results showed that the model was significantly superior to other baselines. In addition, we analyzed the impact of different node and edge weights on model performance.

Reptile Search Algorithm for Association Rule Mining

Reptile Search Algorithm for Association Rule Mining

Association rules forecasting for the foreign exchange market

Several association rule mining algorithms exist, and among them, Apriori is one of the most commonly used methods for extracting frequent item sets from vast databases and generating association rules to gain insights. In this research, we have applied a data mining technique to implement association rules and explore frequent item sets. Our study introduced a model that employs association rules to uncover associations between the foreign exchange market, the gold commodity, and the National Association of Securities Dealers automated quotations (NASDAQ). We suggested a method that used data mining to identify the good points of buying and selling in the foreign exchange market by utilizing technical indicators such as moving average convergence divergence (MACD) and the stochastic indicator to create association rules. The experimental findings indicate that the proposed model successfully generates strong association rules.

Optimizing Marketing Strategies Using FP-Growth and Association Rule Mining Algorithms in the Textile Industry

Optimizing Marketing Strategies Using FP-Growth and Association Rule Mining Algorithms in the Textile Industry

Streamlining Grocery Shopping : Personalized Recommendations for Enhanced Cart Performance

In the burgeoning landscape of e-commerce, the demand for personalized user experiences has become paramount. Traditional methods of online shopping often require users to manually select products for their carts, resulting in a time-consuming and often inaccurate process. In response, this paper presents ShopSculpt, an innovative approach to cart creation that leverages intelligent algorithms to automate product selection based on user interests, profile details, seasonal trends, and weather conditions. Through the integration of predictive modeling and recommendation systems, ShopSculpt aims to streamline the shopping experience, enhancing user satisfaction and engagement. This paper outlines the methodology behind ShopSculpt, presents experimental results demonstrating its efficacy, and discusses its implications for the future of e-commerce. A primary challenge with online shopping is the lack of personalized assistance akin to that found in physical stores. In physical supermarkets, items frequently purchased together are grouped together, encouraging consumers to make additional purchases and thereby boosting sales. We've incorporated this principle into our e-grocery system to create a platform that suggests items to users that they might not have considered previously. Through our efforts, we've established a robust recommendation framework leveraging the Apriori Algorithm for association rule mining and Collaborative Filtering employing the Nearest Neighbors algorithm. We've pinpointed four primary scenarios, encompassing recommendations based on past purchase histories, similarities with other users' preferences, items currently in the user's cart, and top-rated products within the grocery category. This multifaceted approach ensures that our recommendations are tailored to each user's unique needs and preferences.

Simulation of association rule mining based on sensor networks in Chinese language learning recommendation system for college students

Based on the increasing learning needs of students that cannot be met by traditional education in schools, the use of online learning platforms has become a way for many college students to learn Chinese, thereby improving their academic performance and literary literacy. However, it is difficult to guarantee the service quality of online platforms for Chinese learning at present, so this paper proposes an association rule mining algorithm to strengthen Chinese online learning. We using user service quality as a constraint to improve spectrum utilization and energy efficiency, and defining the user state space, borrowing the obtained resource allocation optimization function to reward a small portion of users' communication costs, ultimately obtaining user state space information and one-dimensional state space data. This algorithm performs grouping operations on the data estimated by a large amount of computation, and divides the data into balanced categories to reduce the amount of input data in the network. The performance test results show that this paper has made a great breakthrough in the personalization of Chinese learning, and has outstanding performance in the processing and classification of Big data. There are also some solutions to the problem of too much existing data. In a period of use experience analysis report, we found that the online platform for Chinese learning can give consideration to students' personalized needs and experience.

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.

خوارزمية فصيلة ميركات المعدلة لتعدين القواعد الرابطة

تشكل قواعد جمعيات التعدين (ARM) إحدى تقنيات التنقيب عن البيانات المهمة. أصبحت الأساليب الكلاسيكية التي عمل عليها الباحثون سابقًا غير فعالة للتعامل مع النمو المطرد لقواعد البيانات ، مما دفعنا إلى استخدام عملية التعدين لقواعد الارتباط القائمة على ميتاهيورستك، وفي عملنا سنستخرج جميع القواعد الصحيحة ، و التعدين لا يقتصر على القواعد عالية الجودة. ذكاء السرب يعتبر إحدى هذه الأساليب المعتمدة. في هذه الورقة، تم اقتراح تعديل فصيلة ميركات لتعدين قواعد الجمعية (MCC-ARM). تعتمد الخوارزمية المقترحة بشكل أساسي على خوارزمية فصيلة ميركات (MCA). أكبر فائدة هي تنوع الحلول المرشحة في MCA. سنقوم في عملنا بتمثيل القاعدة باستخدام طريقتين مستعارتين من الخوارزمية الجينية، في المجموعة الأولى، تشير كل مجموعة من القواعد إلى كائن في المجتمع و التي تدعى طريقة بيتسبرغ، بينما تشير القاعدة الثانية إلى كائن في المجتمع و التي تدعى طريقة ميشيغان. تهدف الخوارزمية المقترحة إلى فحص أكبر عدد ممكن من قواعد الارتباط الصحيحة. ما يسمى بخوارزمية تتبع نهج تحديد منطقة البحث الفعالة، والتي تعتمد على آلية عشوائية رئيسية لقيادة الخوارزمية في استخراج القواعد البديلة وتجنب الحلول الكلية من الاسترشاد بنفس القاعدة، وهذا أدى إلى قدر كبير من التنوع. بالإضافة إلى ذلك، يستخدم MCC-ARM طريقة التكثيف في عملية البحث المتجاورة لمنع الخوارزمية من الوقوع في الوضع المحلي. لإثبات كفاءتها، سنطبقها في أربع مجموعات بيانات موثوقة (مثل حديقة الحيوان، والائتمان الألماني، والورم الأساسي والشطرنج). لقد حصل التحسين الذي أحدثته الخوارزمية المقترحة على عاملين حاسمين ، وهما عدد القواعد الصحيحة وقيمة لياقة الجودة.

Course Design Based on Association Rule Mining in Blended Teaching of College English

Course design based on association rule mining in blended teaching of college English offers a data-driven approach to curriculum development, enhancing the effectiveness of English language instruction. By analyzing patterns and relationships within student performance data, association rule mining algorithms identify correlations between different learning activities, topics, and assessment outcomes. This information enables instructors to design courses that strategically integrate online and offline learning components, ensuring a cohesive and engaging learning experience. Additionally, association rule mining facilitates the identification of potential dependencies between course elements, allowing for the optimization of teaching sequences and resource allocation. This paper investigates the use of the Extraction Apriori Rule Mining Blended (EARMB) technique to enhance teaching effectiveness in English instruction within blended learning environments. Through the analysis of association rules derived from instructional data, significant relationships between instructional components and student outcomes are identified, providing valuable insights for course design and instructional strategy development. A structured course design framework is presented, integrating various instructional activities and assessment methods to address diverse language skills and learning objectives. Additionally, a comparative analysis of different teaching approaches, including traditional classroom instruction, online learning platforms, and blended teaching, demonstrates the efficacy of blended teaching in promoting student engagement, improving learning outcomes, and enhancing overall student satisfaction.

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.

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.

Unraveling the patterns of critical contributory factors and flag-state affiliations linked with maritime incident outcomes

Maritime incidents are complex in nature and result from various factors. Although researchers have explored the factors influencing maritime safety, it is unclear how the critical contributory factor chains including flag-state associations, influence maritime incident outcomes, namely deaths, injury, and missing person cases. This paper applies a robust non-parametric Association Rules Mining (ARM) algorithm to an extensive marine incident database to identify critical patterns of key contributory factors and flag-state associations leading to different maritime incident casualty outcomes. The study dataset comprised 13,758 maritime incidents recorded between 2002 and 2015 in US territorial waters. This data was sourced from the Marine Information for Safety and Law Enforcement (MISLE) database system and served as the basis for analysis. A preliminary investigation revealed that low-weight passenger ships were most likely involved in maritime incidents. Further analysis established that, whereas Bahamas-registered vessels were highly likely to be involved in incidents associated with deaths, US-registered vessels were primarily related to injury-related incidents. Singaporean ships were often involved in missing-person incidents. Additionally, the results highlighted significant associations between ocean cruise vessels and existing medical conditions with deaths. Besides, passengers, towing service vessels, and crewmembers were identified to be highly associated with injury. Higher-order associations depicted that tank ships, fishing vessels, ship masters and crew members are likely to be involved in maritime incidents where missing persons are involved. Policies directed at eliminating/controlling the contributory factors identified to lead to a particular outcome to help reduce the occurrence or impacts of maritime incidents have been suggested.

Evaluation of Academic Self-Efficiency, Community Feeling, and Academic Achievement of Students in the Process of the Covid-19 Pandemic by Data Mining Techniques

Thanks to the advancement of technology, vast amounts of data are being generated in various fields on a daily basis. The research on identifying hidden patterns and extracting useful information from big data has become increasingly important. In the field of education, the availability of large datasets has allowed for the emergence of data mining techniques as an alternative to traditional statistical methods. Unlike traditional statistical methods, data mining can uncover hidden relationships between variables, thus avoiding the loss of valuable information and enabling the utilization of essential data in education. By unlocking valuable insights and predicting important relationships, educational data mining (EDM) has the potential to enhance and improve the quality of education. This study aims to demonstrate the predictive power of EDM through a sample application and draw attention to its implications. The dataset used in this study consists of survey responses collected from university students. The variables in the dataset include academic self-efficacy, sense of community, academic achievement averages, and various demographic variables of distance education students. Descriptive modeling was employed to identify latent patterns between variables, while a predictive model was utilized to estimate variables. In order to achieve this, both association rule mining and classification algorithms were employed. The findings of this study indicate that EDM can effectively identify relationships between variables and make accurate predictions.

The Construction of AI-Enabled Mechanism for Preventing Medical Graduates' Employment Psychology Crisis Based on Association Rule Mining Algorithm

The Construction of AI-Enabled Mechanism for Preventing Medical Graduates' Employment Psychology Crisis Based on Association Rule Mining Algorithm

Spatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in Beijing

The intensification of global heat wave events is seriously affecting residents' emotional health. Based on social media big data, our research explored the spatial pattern of residents' sentiments during heat waves (SDHW). Besides, their association with urban functional areas (UFAs) was analyzed using the Apriori algorithm of association rule mining. It was found that SDHW in Beijing were characterized by obvious spatial clustering, with hot spots predominately dispersed in urban areas and far suburbs, and cold spots mainly clustered in near suburbs. As for the associations with urban function areas, green space and park areas had significant effects on the positive sentiment in the study area, while a higher percentage of industrial areas had a greater impact on negative SDHW. When it comes to combined UFAs, our results revealed that the green space and park area combined with other functional areas was more closely related to positive SDHW, indicating the significance of promoting positive sentiment. Subdistricts with a lower percentage of residential and traffic areas may have a more negative sentiment. There were two main combined UFAs that have greater impacts on SDHW: the combination of residential and industrial areas, and the combination of residential and public areas. This study contributes to the understanding of improving community planning and governance when heat waves increase, building healthy cities, and enhancing urban emergency management.

Application Analysis of Nursing Students' Grades in Course Relevance Based on Association Rule Mining Algorithm Apriori

By analyzing the correlation between courses in students’ grades, we can provide a decision-making basis forthe revision of courses and syllabi, rationally optimize courses, and further improve teaching effects. With the help of IBM SPSS Modeler data mining software, this paper uses Apriori algorithm for association rule mining to conduct an in-depth analysis of the grades of nursing students in Shandong College of Traditional Chinese Medicine, and to explore the correlation between professional basic courses and professional core courses. Lastly, according to the detailed analysis of the mining results, valuable curriculum information will be found from the actual teaching data.

Epidemiology of multimorbidity associated with atherosclerotic cardiovascular disease in the United States, 1999–2018

BackgroundThe multimorbidity of Atherosclerotic cardiovascular disease (ASCVD) and many other chronic conditions is becoming common. This study aimed to assess multimorbidity distribution in ASCVD among adults in the United States from 1999 to 2018.MethodsThis cross-sectional survey from the National Health and Nutrition Examination Survey (NHANES) 1999–2018 using stratified multistage probability design. Among the 53,083 survey respondents during the study period, 5,729 US adults aged ≥ 20 years with ASCVD. Joinpoint regression was used to assess the statistical significance of prevalence trends in the prevalence of ASCVD stratified by multimorbidity. The Apriori association rule mining algorithm was used to identify common multimorbidity association patterns in ASCVD patients.ResultsOverall, 5,729 of 53,083 individuals had ASCVD, and the prevalence showed a slow declining trend (biannual percentage change = -0.81%, p = 0.035, average 7.71%). The prevalence of ASCVD significantly decreased in populations without dyslipidemia, diabetes mellitus (DM), hypertension, asthma, chronic obstructive pulmonary disease (COPD), and arthritis (all groups, p < 0.05). Additionally, 65.6% of ASCVD patients had at least four of the 12 selected chronic conditions, with four and five being the most common numbers of conditions (17.9% and 17.7%, respectively). The five most common chronic conditions were (in order) dyslipidemia, hypertension, arthritis, chronic kidney disease, and DM. The coexistence of hypertension and dyslipidemia had the highest support in association rules (support = 0.63), while the coexistence of dyslipidemia, hypertension, metabolic syndrome, and DM had the highest lift (lift = 1.82).ConclusionsDuring the 20-year survey period, there was a significant decrease in the overall prevalence of ASCVD. However, this reduction was primarily observed in individuals without dyslipidemia, DM, hypertension, asthma, COPD, and arthritis. Among populations with any of the evaluated chronic conditions, the prevalence of ASCVD remained unchanged. Most of ASCVD patients had four or more concurrent chronic conditions.