Algorithm For Association Rules Research Articles

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.

Exploration of the methods and rules of syndrome/pattern differentiation and treatment of headache from the acupuncture-moxibustion prescriptions of ancient literature based on the data mining technology.

The study aims to identifying and exploring the methods and rules of the syndrome/pattern differentiation and treatment of headache through collating acupuncture-moxibustion prescriptions recorded earliest in ancient literature. Using Excel2016 software, the structural data table was prepared with "name of disease", "location of disease", "etiology and pathogenesis", "complicated symptoms", "sites for acupuncture and moxibustion" and "techniques of acupuncture and moxibustion" included. The normative approach was conduced on "name of disease", "etiology and pathogenesis", "complicated symptoms" and "nomenclature of acupoint". Using conventional literature statistical method, combined with Apriori algorithm of association rule, the implicit multi-dimensional correlation rules were explored among various elements of syndrome/pattern differentiation of headache and corresponding therapeutic methods. Based on the findings of the study, the regularity was distinct regarding the treatment at "distal acupoints along the affected meridian and the local acupoints at the affected area" after identifying the location of headache; the strong association was presented between "etiology and pathogenesis" and "acupoint selection", and between "etiology and pathogenesis" and "therapeutic methods", including 9 and 12 rules, respectively. Guanyuan (CV 4) selected in treatment of headache was associated with kidney deficiency, the combination of Zhongwan (CV 12) and Zusanli (ST 36) was with phlegm, Fengfu (GV 16), Fengchi (GB 20), Xinghui (GV 22) and Baihui (GV 20) was with wind, and Hegu (LI 4) was with cold. Moxibustion was dominant in treatment if headache was caused by pathogenic cold or related to deficiency syndrome; acupuncture was used specially for the case caused by phlegm, or interaction of wind and phlegm or wind and heat. For heat syndrome, either acupuncture or moxibustion was applicable, in general, acupuncture was more commonly used in comparison with moxibustion for headache. There were 6 association rules regarding the acupoint selection and the techniques of acupuncture and moxibustion. Moxibustion was generally applied to Xinghui (GV 22), Shangxing (GV 23) and Baihui (GV 20) ; and acupuncture was to Fengfu (GV 16), Hegu (LI 4) and Zusanli (ST 36). There were few association rules between the complicated symptoms and acupoint selection. Among nearly 100 complications, there were only 3 feature associations. Zhongwan (CV 12) was selected for the case with poor appetite, Chengjiang (CV 24) was with neck stiffness, and Fengchic (GB 20) combined with Fenglong (ST 40) or Jiexi (ST 41) was used if vertigo was present. In the ancient time, regarding the treatment of headache, acupuncture and moxibustion are delivered based on the three aspects, i.e. the location of illness, the etiology and pathogenesis, and the complicated symptoms. For acupoint selection, in line with the courses of affected meridians, the adjacent and distal acupoints are combined according to the location of headache. The acupoint prescription is composed in terms of the etiology and pathogenesis. The techniques of acupuncture and moxibustion are optimized in consideration of the sites where acupuncture and moxibustion are operated.

Implementation of Association Rules Algorithm to Identify Popular Topping Combinations in Orders

Association rule is a data mining technique to find associative rules between a combination of items. This research aims to apply association rules algorithm in identifying popular topping combinations in food orders. This application aims to help restaurant owners or food businesses understand their customers' preferences and optimize their menu offerings. Data obtained from kaggle, the association rules algorithm is applied to this dataset to identify patterns or combinations of toppings that often appear together in orders. The results of this study show toppings with chocolate as a popular item in orders. These findings can provide valuable insights for food business owners in structuring their menus and determining attractive offers for customers. This study also applied a comparison between the apriori, fp- growth and eclat algorithms, with the result that the best item transaction rule was found: a combination of dill & unicorn toppings with chocolate with 60% confidence. Overall, the application of eclat algorithm in this study provides the best performance with higher execution speed, thus providing insight into customer preferences regarding topping combinations in food orders. Despite the shortcomings of the data form from this study, it is expected to help business owners in optimizing their offerings, increasing customer satisfaction, and improving their business performance.

The value of Association Rule Analysis in understanding serious and fatal road traffic crashes - a case study of the N4 toll road between 2015 and 2019

In spite of the fact that the road traffic crash fatality rate in South Africa is significantly higher than the global average, the characteristics and causes of road traffic crashes are still not well understood. Without a clear understanding of the characteristics and causes, intelligence-led countermeasures to reduce crashes cannot be developed or applied. The analysis of local South African road crash data is of particular importance in identifying the underlying problems responsible for the high crash rates across the country. While descriptive analyses can be used to present a snapshot of the crash problem, they are largely inadequate when it comes to throwing light on the underlying patterns and relationships between the contributing factors and causes of the crashes themselves. This paper presents a study of the use of Association Rule Analysis (ARA) on data related to crashes on the N4 between Pretoria and the Mozambican border, between 2015 and 2019, to see what added value ARA may offer to standard descriptive analyses of crash data. Apriori Association Rule algorithm was employed on the dataset to identify what contributory factors were not initially evident in the occurrence of serious and fatal crashes. ARA revealed that pedestrians, negligent driving, overtaking and single-vehicle crashes are common factors likely to be present in a serious crash. However, this study found that descriptive statistical analysis and ARA methods are not mutually exclusive, but rather complementary. Where descriptive statistical analysis focuses on summarising and describing data, ARA focuses on identifying relationships and patterns within the data.

Apriori Algorithm Application for Consumer Purchase Patterns Analysis

The Apriori algorithm is a data mining association rule algorithm for finding relationship patterns between one or more items in a dataset. Apriori algorithm is often used in transaction data analysis or market basket analysis. Apriori algorithm is used to find out consumer purchase patterns in e-commerce systems and provide product recommendations to consumer by extaracting associations or events from transactional data. This study is purposed for deeply analyze the steps, performance of Apriori algorithm, and give relevant an example of case study to better explain the steps of Apriori algorithm application, as well as the results achieved.

Air Quality Correlation Analysis Based on Apriori Algorithm

In recent years, air pollution has become an environmental issue of global concern. It not only affects human health, but also causes serious damage to the Earth's ecosystem. By analyzing the concentration values of various pollutants in the air and the air quality index (AQI) every hour, the most relevant components to the AQI can be identified. In this paper, Apriori association rule algorithm is applied to correlate various data in air quality monitoring to provide a new reference solution for solving environmental pollution problems in the future.

MARKET BASKET ANALYSIS MENGGUNAKAN ALGORITMA HASH BASED PADA DATA PENJUALAN DI O2 SWALAYAN

O2 Supermarkets have stock of goods available in the warehouse. During this time there is a buildup of goods in the warehouse because there is no system to analyze transaction data so that losses occur. Therefore, a system is needed to manage these goods in order to reduce stocks of goods that are not selling well and make stocks of goods that sell well in warehouses through consumer spending patterns. To analyze data on consumer spending patterns, the Market Basket Analysis Method can be used. This method can be used to analyze consumer spending patterns. The algorithm used is the Hash Based and Association Rule Algorithm because it can overcome the determination of frequent itemsets from k-itemset candidates with a large number and speed up information on consumer spending patterns. With existing data and using this algorithm it can produce a lot of information. The results of this study get 6 Rules with a minimum Confidence of 50% and minimum Support>= 4. From consumer spending patterns that can be used as a strategy to support sales, knowing which items are selling best, placing goods so that they are more efficient in picking up the goods desired by consumers, not only shopping patterns but also more controlled stock of goods.

ANALYSIS OF THE INFLUENCE OF BUSINESS INTELLIGENCE ON BEVERAGE SALES KONNICHIWA COFFEE USING THE METHOD EQUIVALENCE CLASS TRANSFORMATION

Konnichiwa Coffee shop is one of the business beverage that sells various types of drinks. Determination of price discounts or product promotions sometimes doesn’t match what customers wants and needs. Another obstacle found was that there were no promotions for consumers who bought directly at Konnichiwa Coffee outlets. This causes less than optimal sales strategies and store promotion strategies. Determining menus that are often purchased simultaneously by consumers can be a reference for owners in determining promotional strategies. Therefore, this research was conducted to look for association patterns between menus that can implement business intelligence (BI) in the association rules method. One of the association rules algorithms is the ECLAT algorithm. The ECLAT algorithm is used because it is more efficient and faster in terms of time. The data used in this research were 214 products from 100 transactions with 26 types of drink menus. The resulting pattern refers to a minimum support value of 3% and a minimum confidence of 30%. This means that transaction data that has association patterns or that were purchased together is only 3% of the total transaction data with a confidence level of 30%. From the results obtained, the Java Latte, Kopi Latte and Sapporo Latte menus are the menus that are most often purchased together so they can be used as a marketing strategy for Konnichiwa Coffee.

Research on Accounting Quality Management of Enterprises under Financial Sharing Mode

Abstract With the deep integration of the global economy and the continuous individuality of technology, the complexity of enterprise operation and management is increasing, which makes the traditional accounting and accounting methods difficult to meet the needs of modern enterprises. This paper proposes an enterprise accounting accounting method applying information computing technology, which applies association rules to generate frequent item sets of data for the accounting data under the financial sharing mode and obtains the optimal rules through pruning. Based on the time series prediction of the enterprise’s financial data, changes are made. Taking the actual enterprise case study, the association rules involved in accounting are more accurate, the calculation results are basically the same as the actual value, and the final cost accounting value is only nearly 2 million yuan different from the actual value, the accounting error is only 0.02%. Furthermore, the association rule algorithm has a significant impact on improving the efficiency of accounting work. The service time of counter personnel can be reduced by 30% after Bank P applies the management rules to accounting management. The average execution time of the initial ticket examination operation has been accelerated from 10.53 seconds to 5.42 seconds. The association rule algorithm can effectively improve the precision of enterprise accounting and the efficiency of accounting personnel, while also identifying existing problems in the current management system.