Association Rule Mining Algorithm Research Articles

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.

Unsupervised Learning in NBA Injury Recovery: Advanced Data Mining to Decode Recovery Durations and Economic Impacts

This study utilized advanced data mining and machine learning to examine player injuries in the National Basketball Association (NBA) from 2000–01 to 2022–23. By analyzing a dataset of 2296 players, including sociodemographics, injury records, and financial data, this research investigated the relationships between injury types and player recovery durations, and their socioeconomic impacts. Our methodology involved data collection, engineering, and mining; the application of techniques such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), isolation forest, and the Z score for anomaly detection; and the application of the Apriori algorithm for association rule mining. Anomaly detection revealed 189 anomalies (1.04% of cases), highlighting unusual recovery durations and factors influencing recovery beyond physical healing. Association rule mining indicated shorter recovery times for lower extremity injuries and a 95% confidence level for quick returns from “Rest” injuries, affirming the NBA’s treatment and rest policies. Additionally, economic factors were observed, with players in lower salary brackets experiencing shorter recoveries, pointing to a financial influence on recovery decisions. This study offers critical insights into sports injuries and recovery, providing valuable information for sports professionals and league administrators. This study will impact player health management and team tactics, laying the groundwork for future research on long-term injury effects and technology integration in player health monitoring.

Research on the Path Construction and Practice of Bidirectional Cultural Mutual Guidance in Higher Vocational English Course Civics Teaching under the Background of Big Data

Abstract This study delves into the effective integration of bi-directional cultural intercultural instruction with Civic and Political teaching in a higher vocational English course and analyzes it with an association rule mining algorithm. By screening the weighted candidate itemsets through the weighted support degree model, the study identifies the itemsets’ positive and negative correlation attributes and mines the association rules between English teaching and students’ bidirectional cultural interduction ability by combining with the decision support model. The article also analyzes the content of Civics and Chinese and foreign cultural elements in the English curriculum. It compares and examines the changes in students’ two-way cultural core literacy and its introduction effect. The results of the study show that after the bi-directional cultural Civics teaching in the English curriculum, students experienced significant changes in worldview, values and Chinese and foreign cultural cognition, with t-values of −4.312, −5.034, and −4.275, respectively, and p-values of less than 0.001. In addition, the students’ bi-directional cultural communication competence improved significantly, with scores ranging from 3.8 to 4.3. The study provides valuable insights into the promotion of Chinese-foreign cultural communication and the reform of higher vocational English teaching.

Association rule mining with a special rule coding and dynamic genetic algorithm for air quality impact factors in Beijing, China.

Understanding air quality requires a comprehensive understanding of its various factors. Most of the association rule techniques focuses on high frequency terms, ignoring the potential importance of low- frequency terms and causing unnecessary storage space waste. Therefore, a dynamic genetic association rule mining algorithm is proposed in this paper, which combines the improved dynamic genetic algorithm with the association rule mining algorithm to realize the importance mining of low- frequency terms. Firstly, in the chromosome coding phase of genetic algorithm, an innovative multi-information coding strategy is proposed, which selectively stores similar values of different levels in one storage unit. It avoids storing all the values at once and facilitates efficient mining of valid rules later. Secondly, by weighting the evaluation indicators such as support, confidence and promotion in association rule mining, a new evaluation index is formed, avoiding the need to set a minimum threshold for high-interest rules. Finally, in order to improve the mining performance of the rules, the dynamic crossover rate and mutation rate are set to improve the search efficiency of the algorithm. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the unit point multi-information coding strategy in reducing the rule storage air, the effectiveness of mining the rules formed by the low frequency item set, and the effectiveness of combining the rule mining algorithm with the swarm intelligence optimization algorithm in terms of search time and convergence. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the above three aspects. The unit point multi-information coding strategy reduced the rule space storage consumption by 50%, the new evaluation index can mine more interesting rules whose interest level can be up to 90%, while mining the rules formed by the lower frequency terms, and in terms of search time, we reduced it about 20% compared with some meta-heuristic algorithms, while improving convergence.

A statistical study on the similarities and differences of sports interests among college students

Abstract This study focuses on the status quo of group exercise interest differences and similarities among college students, and proposes the interest scalar law of group exercise for the group exercise data of college students, which measures the frequency of group exercise based on the parameter and activity. Based on the interest degree indexes of exercise duration and time, the interest degree model of group exercise is constructed to explore the laws of group exercise behavior. An association rule mining algorithm based on genetic algorithms and interest degrees is proposed, and an improved fitness function is proposed to optimize the algorithm. Taking China’s province S as an example, the statistics on group sports interests of college students show that the central tendency among group sports interests is ball group sports. Among the influencing factors of interest dissimilarity, the gender influencing factor significantly differs in the dimensions of psychological state and peer influence (p&lt;0.05). There were significant differences (p&lt;0.05) and highly significant differences (p&lt;0.01) in the p-values of the place of origin influence factor in the course offerings and psychological state dimensions, respectively. The group sport type influence factor, on the other hand, showed significant differences (p&lt;0.01) in all four sizes, including psychological state and family status.

ADSA – Association-Driven Safety Analysis to Expose Unknown Safety Issues

Autonomous systems are susceptible to unknown safety issues due to overlooked dependencies among components of the system and the entities that are part of its operating environment. The current safety analysis techniques aids in identifying known safety issues but not overlooked/unknown safety issues. To identify unknown safety issues due to problematic interactions between components, in our previous work, we proposed safety assessment for concurrent components (SACC). Despite being more effective than FMEA and goal modeling, SACC suffers from some limitations such as not considering environmental entities and their properties, and a manual process for identifying associated components for the collective analysis. For a complex system with a large number of components, such an analysis can result in overlooking safety issues. To address these limitations, in this paper, we propose an association-driven safety analysis (ADSA) approach, which is extended and built on SACC. The approach uses a property-relation (PR) table and modified association rule mining algorithm to identify components and environmental entities that need to be considered together to detect overlooked or unknown safety issues. We evaluated our approach using four robotic systems and compared with SACC and systems theoretic process analysis (STPA). Our results show that our proposed approach, in particular using behavioral dependencies, is effective at exposing unknown safety issues.

Research on Intelligent Recommendation of Computer Course Items Combining Augmented Learning Algorithms and Data Mining Techniques

Abstract This paper combines augmented learning algorithms and data mining techniques for an intelligent recommendation system for computer course projects to optimize the allocation of learning resources and improve learning efficiency and effectiveness. The research methodology includes an improved association rule mining algorithm and an augmented learning model applying deep Q-network and adaptive reward function. The research results show that the system can effectively mine knowledge point associations and achieve personalized recommendation. In particular, the accuracy of knowledge point associations and the customized level of recommendation are improved by multi-support strategy improvement and knowledge point association correction. Experiments show that on the edX dataset, the recommendation recall reaches 0.347 at k=10 and 0.433 at k=20, significantly better than the traditional recommendation algorithm. This intelligent recommendation system can effectively improve the learning effect of computer course projects, and it has a positive impact on both learning attitude and independent learning ability, especially helping the backward students significantly and reducing the learning differences between classes.

An Algorithm for Data Management of Higher Education Based on Fuzzy Set Theory - Association Rule Mining Algorithm

An Algorithm for Data Management of Higher Education Based on Fuzzy Set Theory - Association Rule Mining Algorithm

A novel software defect prediction approach via weighted classification based on association rule mining

Software defect prediction technology is used to assist software practitioners in effectively allocating test resources and identifying hidden defects in a timely manner. However, the prediction of defect-prone software using association rule mining algorithms is limited because of the unbalanced distribution of defect data. Furthermore, although the existing weighted association rule mining approach considers item strength, the weight calculation still relies on expert experience and lacks fine granularity. We propose a novel software defect prediction approach based on mutual information and correlation coefficient weighted class association rule mining (MCWCAR). The MCWCAR model employs a cost-sensitive strategy and generates frequent itemsets according to three mining objectives while maintaining the original item distribution: defective class rules, non-defective class rules, and feature association relationships. During the weighted frequent itemset mining process, it combines feature selection and itemset screening to determine the appropriate feature combination through mutual information weighted support. Meanwhile, the correlation coefficient is applied to accurately depict the correlation between feature items and defect classes, serving as the weight to mine class association rules. Additionally, to ensure that interestingness measures have asymmetry and effectively represent negative associations under the condition of class imbalance, the addedvalue is adopted in the filtering association rules. We conducted experiments on 27 open-source datasets and evaluated the performance differences between MCWCAR and state-of-the-art baseline classifiers. Experimental results demonstrate that the proposed algorithm significantly outperforms other baselines in terms of Balance, Gmean, MCC, and F-measure.