Algorithm For Association Rules Research Articles

Prompt Update Algorithm Based on the Boolean Vector Inner Product and Ant Colony Algorithm for Fast Target Type Recognition

In recent years, data mining technology has become increasingly popular, evolving into an independent discipline as research deepens. This study constructs and optimizes an association rule algorithm based on the Boolean vector (BV) inner product and ant colony optimization to enhance data mining efficiency. Frequent itemsets are extracted from the database by establishing BV and performing vector inner product operations. These frequent itemsets form the problem space for the ant colony algorithm, which generates the maximum frequent itemset. Initially, data from the total scores of players during the 2022–2024 regular season was analyzed to obtain the optimal lineup. The results obtained from the Apriori algorithm (AA) were used as a standard for comparison with the Confidence-Debiased Adversarial Fuzzy Apriori Method (CDAFAM), the AA based on deep learning (DL), and the proposed algorithm regarding their results and required time. A dataset of disease symptoms was then used to determine diseases based on symptoms, comparing accuracy and time against the original database as a standard. Finally, simulations were conducted using five batches of radar data from the observation platform to compare the time and accuracy of the four algorithms. The results indicate that both the proposed algorithm and the AA based on DL achieve approximately 10% higher accuracy compared with the traditional AA. Additionally, the proposed algorithm requires only about 25% of the time needed by the traditional AA and the AA based on DL for target recognition. Although the CDAFAM has a similar processing time to the proposed algorithm, its accuracy is lower. These findings demonstrate that the proposed algorithm significantly improves the accuracy and speed of target recognition.

Path analysis of coal mine accident risk factors based on the 24Model.

Coal mine accidents seriously affect China's coal safety production and sustainable development. The present study aimed to reveal the risk factors in coal mine accidents and explore the causal relationship among risk factors. This study utilized text mining to analyse 450 coal mine accident reports, identifying 50 risk factors and efficiently mapping them into the 24Model. The association rule algorithm was then used to mine the strong association rules among the risk factors within the 24Model, establishing the interaction mechanism among them. Based on the strong association rules, related hypotheses were proposed. Finally, the hierarchical and logical relationships of risk factors within the 24Model were analysed, and the causal and mediating effects were tested by path analysis. The safety management system has a direct effect on unsafe acts, unsafe conditions, habitual behaviour and organizational safety culture. Moreover, external influence has an effect on unsafe acts, organizational safety culture and habitual behaviour through the mediating effect of the safety management system. Based on the results obtained, this study proposes a series of specific measures to prevent risks in coal mines, providing a new perspective for the analysis and prevention of accidents.

Web log mining techniques to optimize Apriori association rule algorithm in sports data information management

To optimize the current college sports data information management system, this study combines the Apriori association rule algorithm with web application development technology to upgrade the management system. Firstly, this study explores novel log mining techniques in genetic algorithms and web application development technology. Secondly, by integrating log mining techniques to optimize the Apriori algorithm, associations between sports data and information are discovered. Through the optimized algorithm, this study identifies key association rules of sports data information and validates the optimized system’s reliability and effectiveness through experiments. The experimental results show that the running time of the traditional Apriori algorithm exponentially grows with the increase in information volume, while the optimized execution efficiency is improved by approximately 10–15%. Additionally, the average retrieval accuracy of this optimized system can reach 98.3%, although the retrieval time also increased by 23%. Therefore, the technology and algorithms proposed in this study have certain application value in the sports information management system and contribute to the optimization of data information management in this field.

Study on Audit Risk Model Based on Data Mining Algorithm

With the widespread application of modern information technologies such as big data, enterprises have generated a large amount of data in their daily operations. The traditional audit methods have drawbacks such as high cost and high consumption, and can no longer meet the needs of audit work in the digital era. Therefore, it is urgent to adopt audit methods suitable for the digital era to improve audit quality and reduce enterprise Audit risk. For professional auditors in enterprises, it is very important to utilize emerging technologies such as data mining algorithms. The audited enterprise may tamper with its financial statements, and identifying high-quality audit data from massive amounts of data is a huge challenge. Compared to traditional audit methods, data mining algorithms can significantly improve the efficiency and accuracy of audit work. For example, the BP neural network, with its powerful nonlinear mapping ability, can capture complex relationships in data; Support vector machines classify data by finding the optimal hyperplane in high-dimensional space and have good generalization ability; random forest reduces overfitting and improves prediction accuracy by integrating multiple decision trees; Association rule algorithms can discover interesting relationships between data items, helping auditors identify potential fraudulent behavior or abnormal transactions. Therefore, the purpose of this study is to accurately evaluate and reduce the audit risk of enterprises, and to build an audit risk model using computer data mining algorithms. This provides necessary reference and guidance for auditors to conduct data analysis and mine valuable data during the audit process of enterprises, thereby improving audit efficiency.

Application of association rules and simulated annealing algorithms in optimizing traditional Chinese medicine placement schemes

Background and aimChina has used traditional Chinese medicine (TCM) to treat diseases for more than 2000 years. Traditionally, TCMs in medicine cabinets are arranged alphabetically or on the basis of experience, but this arrangement greatly affects dispensing efficiency. However, owing to the unique properties and qualities of TCM, very few automatic approaches or systems have specifically addressed TCM dispensing problems. Therefore, it is necessary to establish a method of optimizing the traditional Chinese medicine placement scheme (TCMPS) via computer algorithms to improve the work efficiency of pharmacists.MethodsA prescription dataset from a hospital in 2022 was obtained, and the association rule algorithm (ARA) was used to calculate the frequency of use for each type of TCM and the associations between different types of TCMs. On the basis of these association and frequency data, the optimal TCMPS was calculated using the simulated annealing algorithm (SAA) and then verified using the prescription dataset from 2023.ResultsA total of 10,601 prescriptions were collected in 2022, involving 360 different TCMs, and each prescription contained an average of 9.485 TCMs, with Danggui (3628) being the most frequently used. When the threshold of support was set to 0.05 and the confidence was set to 0.8, 78 couplet medicines used in orthopedics clinics were found through ARA. When the threshold value of support was set to 0, the confidence was set to 0, and the rule length was 2, a total of 129,240 rules were obtained, indicating support between all pairwise TCMs. The TCMPS, calculated using SAA, had a correlation sum of 14.183 and a distance sum of 3.292. The TCMPS was verified using a prescription dataset from 2023 and theoretically improved the dispensing efficiency of pharmacists by approximately 50%.ConclusionsIn this study, the ARA and SAA were successfully applied to pharmacies for the first time, and the optimal TCMPS was calculated. This approach not only significantly improves the dispensing efficiency of pharmacists and reduces patient waiting time but also enhances the quality of medical services and patient satisfaction, and provides a valuable reference for the development of smart medicine.

A three-stage prediction model for firm default risk: An integration of text sentiment analysis

Predicting firm default risk is vital for financial institutions to avert significant economic losses, making the enhancement of its prediction precision both imperative and intricate. This research introduces a three-stage prediction model, including association rule algorithm (ARA), support vector machine (SVM) and modified particle swarm optimization algorithm (MPSO). Features selected by ARA are used as inputs for SVM, and penalty parameter and kernel parameter of SVM is optimized by MPSO that uses the adaptive inertia weight. The importance of text sentiment variables are emphasized to predict firm default risk. In the first stage, feature selection seeks to curtail the dimensions of both financial and non-financial variables. The empirical findings validate the efficacy of the ARA, revealing a strong correlation between text sentiment and default risk. The subsequent two stages deploy the SVM, refined by the MPSO, to predict the default risk. Compared with renowned models, the proposed model displays superior prediction precision and a reduced computational overhead. This research furnishes a potent instrument for regulators and firms alike, aiding in mitigating prospective default risks and forestalling broader economic upheavals.

State Evaluation of Electrical Equipment in Substations Based on Data Mining

This paper explores the combination of a data mining-based state evaluation method for electrical equipment in substations, analyzing the effectiveness and accuracy. First, a Gaussian mixture model is applied to fit all raw data of electrical equipment. The Expectation Maximization algorithm summarizes the data distribution characteristics and identifies outliers. The a priori algorithm is then employed for data mining to derive frequent itemsets and association rules between equipment quality and measurement data. For new equipment samples, conditional probabilities of each feature are independently calculated and combined to classify and evaluate equipment quality. The results suggest that equipment reliability in smart substations can be inferred from historical and real-time operational data using improved association rule algorithms and Naive Bayes classifiers. Finally, the proposed method was applied to analyze statistical data from a 110 kV substation of a power supply company. The states prediction accuracy exceeded 95% when compared with actual equipment quality. The effectiveness evaluation metrics demonstrated that this method outperforms single-category algorithms in terms of accuracy and discrimination ability.

DNN-Based Resource Recommendation for Ideology Theory Courses Online

Revamped IPE confronts static material constraints and outdated pedagogy, warranting integration of web resources and big data analytics for instructional innovation. Digital IPE adoption in vocational education optimizes online resource use, enhancing teaching effectiveness. Introducing CUPMF, a personalized learning model, we conduct empirical assessments on a large dataset (364,617+ entries) from Smart Classroom's cloud platform and public datasets, reflecting varied IPE scenarios. Comparative experiments against association rule, content-, tag-based, and collaborative filtering algorithms show CUPMF's superiority. It achieves a 11.61% F1 score boost over four alternatives for basic recommendations and outperforms Que Rec by 1.975%. Complexity-wise, CUPMF registers an 11.52% mean F1 score increment over four methods and 1.875% over Que Rec. Proven, CUPMF markedly improves IPE resource recommendation accuracy and efficacy, poised to transform personalized online vocational learning.

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.

Data mining techniques on astronomical spectra data – III. Association analysis

ABSTRACT Association analysis is an important task that aims to investigate correlations in astronomical spectral data and mine relationships between different data features. With the rapid development of various sky survey projects, multiple association analysis methods have been applied to efficiently investigate the correlation between spectral data. However, due to the different focuses of analysing the relationships within spectral data, the performance and applicability scenarios of association analysis methods vary. We present the third article in the series to provide a comprehensive review of algorithms for astronomical spectral association analysis. First, this paper outlines the ideas and applications of association analysis algorithms for astronomical spectra in the current literature. Secondly, experiments are conducted on a unified A-type stellar spectral data set constructed based on three different signal-to-noise ratios and data volumes to examine the performance of different algorithms in analysing the correlation between data features. The results indicate that association rule algorithms can more comprehensively and effectively uncover the correlations among different spectral features, while regression analysis algorithms offer a simpler and more intuitive approach to analysing relationships between features. The spectral data used in the experiments are obtained from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope. Finally, the source code of association analysis algorithms and manuals for usage are provided on GitHub.

Electric vehicle load forecasting based on convolutional networks with attention mechanism and federated learning method

AbstractAccurate forecasting of electric vehicle (EV) load is essential for grid stability and energy management. EV load forecasting is influenced by multiple factors. At present, the load forecasting model for EVs mainly uses collected sample data to build a data‐driven model. But these algorithms need to collect all the data together to train the model, ignoring the privacy of each data collection source. In a competitive market environment, each device service provider is not willing to share the sample data they store. Aiming at this problem, this paper proposes an EV load diagnosis algorithm considering data privacy. Firstly, a convolutional neural network with dual attention mechanism is constructed as the basic time series forecasting model. The association rule algorithm is used to select weather data with strong associations as the inputs of the model. Each service provider uses local data to perform deep learning network. All models are then trained using a federated learning framework. During the entire training process, historical data is stored locally, and only model parameter information is shared and interacted; thus data privacy is protected. Finally, the validity of the algorithm in this paper is verified by using real collected EV load data.

Research on Evaluating Information Security Level Protection by Data Mining Method

Information technology’s continuous development and progress can bring great changes to society, which is also born in the hierarchical protection of information systems. Among them, grade protection evaluation is the key part, and methods to find useful information from its complicated evaluation data to assist decision-making have become an important content of research. Aiming at the evaluation process of data mining, this article intends to analyze the classical algorithms of association rules. The Apriori algorithm and FP-growth, two common and representative algorithms, are chosen as a comparison. Finally, it is found that the performance of the latter is better than that of the former. The rationality of association rules is proved by analysis to better assist the evaluators in the evaluation work and reduce the misjudgment of the evaluation results.

Data mining-based decision support system for educational decision makers: Extracting rules to enhance academic efficiency

The exponential growth of data in the field of education has created a pressing demand for a robust system capable of analyzing this vast amount of information, empowering decision-makers to enhance academic efficiency. By accurately identifying student eligibility for specific courses at the outset, a multitude of challenges, such as dropouts and poor performance, can be effectively mitigated or even prevented. This paper presents a Decision Support System (DSS) that leverages a hybrid data mining model to analyze educational data and unveil concealed patterns and rules. The DSS aims to support decision-makers in enhancing the operational effectiveness of academic institutions by enabling accurate determination of student eligibility for specific courses. To ensure robust analysis, diverse supervised algorithms were implemented, comparative evaluations were conducted, and association rule and unsupervised algorithms were employed to extract hidden patterns. In a case study involving graduates pursuing Master's degrees in Iran, the hybrid model effectively identifies Bachelor's degree majors that exhibit higher likelihoods of success in the targeted course and uncovers additional factors that significantly impact academic performance. The research outcomes highlight the considerable potential of the hybrid model in facilitating informed scholarship decisions and fostering overall institutional efficiency. Furthermore, the proposed DSS could be deployed with various educational datasets, potentially empowering decision-makers with valuable insights to distinguish between successful and unsuccessful students early in the course. This could contribute to optimizing educational outcomes and informing evidence-based policy-making.

Exploration Vectors and Indicators Extracted by Factor Analysis and Association Rule Algorithms at the Lintan Carlin-Type Gold Deposit, Youjiang Basin, China

The Youjiang Basin in China is the world’s second-largest concentrated area of Carlin-type Au deposits after Nevada, USA, boasting cumulative Au reserves nearing 1000 t. This study examined the recently unearthed Lintan Carlin-type Au deposit within the Youjiang Basin. Factor analysis and association rule algorithms were used to identify exploration vectors and indicators essential for navigating this promising geological territory. In the Lintan mining area, the geological strata encompass the Triassic Bianyang, Niluo, and Xuman formations comprised clastic rocks, followed by the deeper Permian Wujiaping Formation with massive carbonate rocks. The orebodies are restricted to the F14 inverse fault, cutting through the Xuman Formation, with an additional F7 fault between the Wujiaping and Xuman formations. A total of 125 rock samples from the F14 fault and a representative cross-section were analyzed for 15 elements (Au, Ag, As, Bi, Cd, Co, Cu, Hg, Mo, Ni, Pb, Sb, Tl, W, and Zn). The elements were divided into four groups based on cluster and factor analysis. Group 1 (Co, Cu, Zn, Ni, Tl, W, and Bi) was mainly enriched in the Xuman, Niluo, and Bianyang formations controlled by sedimentary diagenesis. Group 2 (Au, As, Hg, and Sb) was concentrated in the F14 and F7 faults, representing Au mineralization. Group 3 (Pb, Ag, and Mo) was mostly enriched near the F14 and F7 faults, displaying a peripheral halo of Au mineralization, and was probability controlled by ore-forming hydrothermal activities. Group 4 (Cd and Mo) exhibited extreme enrichment along the periphery of the F7 fault. This pattern indicates the presence of a substantial hydrothermal alteration zone surrounding the fault, likely influenced by ore-forming hydrothermal processes. Additionally, Pb, Ag, Cd, Mo, and W are considered essential indicators for ore formation besides Au, As, Sb, Hg, and Tl. Twelve effective association rules were derived using the association rule algorithm, which can aid in discriminating Au mineralization. The spatial distributions of the 15 elements indicated that the F14 fault is the main ore-bearing fracture zone, while the F7 fault serves as the ore-conducting structure, channeling ore-forming fluids into the F14 fault. Faults between the Wujiaping and Xuman formations, along with their associated reverse faults, present potential prospecting targets both within and outside the Lintan Au deposit in the Youjiang Basin. Exploration geochemical data can be fully utilized by combining factor analysis and association rule algorithms, offering key guidance for prospecting Carlin-type gold and similar deposits.

Application of Improved Association Rule Algorithm in Teaching Management Systems of Colleges and Universities

Teaching management systems (TMS) are comprehensive platforms designed to streamline various aspects of educational administration, instruction, and communication within academic institutions. These systems typically offer features such as course scheduling, grade management, attendance tracking, and communication tools for instructors, students, and administrators. TMS also facilitates content delivery, assessment creation, and student progress monitoring, providing a centralized hub for all aspects of teaching and learning. With the integration of modern technologies like cloud computing and mobile applications, TMS enhance accessibility, efficiency, and collaboration among stakeholders in education. This paper explores the integration of Frequent Pattern Decision Support Systems (FP-DSS) into Teaching Management Systems (TMS) and its impact on teaching and learning practices in higher education. Through a comprehensive experimental analysis, we investigate the effectiveness of FP-DSS in improving learning outcomes, personalization, and student engagement across various teaching methodologies. The findings reveal significant improvements in learning outcomes, with average exam scores increasing by up to 12% when FP-DSS is incorporated into innovative teaching methodologies. Additionally, we observed enhanced personalization of instruction, with a rating of 9 out of 10 for the effectiveness of FP-DSS in tailoring learning experiences to individual student needs. Furthermore, student engagement showed notable improvements across all experiments, with students actively participating in the learning process and demonstrating higher levels of motivation and interest.

Cultural tourism attraction recommendation model based on optimized weighted association rule algorithm

To address the challenge of users selecting rich tourism resources, this study proposes a model for cultural tourism attraction recommendation using an optimized weighted association rule algorithm. This model includes time and season weight for tourist attraction recommendations. This model proposes improvement methods to address some inherent issues in traditional tourism recommendation models. Firstly, it constructed a recommendation model for cultural tourism attractions, and then optimized the weighted association rule algorithm by incorporating dynamic time weights. It takes into account the user's intended time in the recommendation outcome. Moreover, it incorporated seasonal weights to optimize the weighted rule algorithm for factors such as user travel time and the attractions' peak season during the recommendation process. The experiment indicates that the F1 value of the improved algorithm model proposed in this study reaches 0.952, the accuracy reaches 0.985, the area under the curve reaches 0.955, the Recall value reaches 0.812, and the fitting degree reaches 0.971. The results suggest that the proposed cultural tourism attraction recommendation model, based on an optimized weighted association algorithm, performs well in recommending tourist destinations. This model can have a positive impact on the development of the tourism industry.

Research on the Application of Improved Apriori Algorithm in Sport-Adaptive Online Teaching System Under Big Data Environment

Each student in an adaptive education system has significant differences in knowledge background, ability level and cognitive style. Therefore, to build an adaptive teaching system, it is necessary to establish an operable, reasonable and individualized student model by clarifying students’ abilities and differences. The improved Apriori algorithm under big data is the most classic association rule algorithm, which is generated by a set of candidates, and it uses the iterative method of hierarchical search to traverse a set of frequency items in the transaction database. After finding the set of frequency items, select the association according to the trust rules. This paper studies how to apply the improved Apriori algorithm to an adaptive online education system in a big data environment. An evolutionary algebra is taken with mean fit of 80, population size of 20, mean fit of 0.28, population size of 60, mean fit of 0.26, population size of 80, mean fit of 0.25, mean population size of 0.25. The population size is 200, and the average fitting is 0.24. The larger the error, the smaller the error between each indicator of the test paper and the corresponding value specified by the user. The improved Apriori algorithm in the big data environment has designed five themes of rule mining, which are mainly used for class management: class linkage, class category linkage, student basic information linkage, lecture and basic information linkage, and lecture mode linkage. They play the role of teaching assistants with a specific role.

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.

Unsupervised machine learning approach for tailoring educational content to individual student weaknesses

By analyzing data gathered through Online Learning (OL) systems, data mining can be used to unearth hidden relationships between topics and trends in student performance. Here, in this paper, we show how data mining techniques such as clustering and association rule algorithms can be used on historical data to develop a unique recommendation system module. In our implementation, we utilize historical data to generate association rules specifically for student test marks below a threshold of 60%. By focusing on marks below this threshold, we aim to identify and establish associations based on the patterns of weakness observed in the past data. Additionally, we leverage K-means clustering to provide instructors with visual representations of the generated associations. This strategy aids instructors in better comprehending the information and associations produced by the algorithms. K-means clustering helps visualize and organize the data in a way that makes it easier for instructors to analyze and gain insights, enabling them to support the verification of the relationship between topics. This can be a useful tool to deliver better feedback to students as well as provide better insights to instructors when developing their pedagogy. This paper further shows a prototype implementation of the above-mentioned concepts to gain opinions and insights about the usability and viability of the proposed system.

Song Emotion Intelligence Analysis for Psychological Stress Relief

In today's digital and networked era, multimedia content such as images, audio, and video have become an important part of the data transmitted on the Internet's information superhighway. How to manage the stress in the college student population is directly related to the future life and development of college students. In this article, based on domestic and international data mining technology, the authors designed an intelligent analysis system for college students' mental health, pre-processed the data, then analysed these data in detail by using the outlier analysis algorithm in clustering algorithm, and finally mined the intrinsic connection between the psychological problems and attributes by using the Apriori association rule algorithm, so as to provide the decision makers with a reliable basis. This study puts forward reasonable solutions and suggestions for the problems existing in the psychological level of contemporary college students.