Association Rule Model Research Articles

Developing a model of association rules with machine learning in determining user habits on social media

The object of the research is the habits of social media users. The problem in this research is that there is a lot of data spread across social media platforms ranging from image data, text data to audio data, making it difficult to identify starting from user behavior patterns, user interest in certain things and user habits. This research aims to explore scattered text data as text data has not been analyzed deeply in terms of word structure, thus concealing a lot of information and making it difficult to conduct analysis to determine user patterns and behavior on social media. The results obtained from this research are in the form of analysis and models that can identify and understand user patterns and behavior on social media using association rules and machine learning approaches. In applying the association technique, an a priori algorithm is used, which in the process determines all text data into item sets so that it can identify the habits of social media users and in the machine learning process there is a model formation process so that the results can be compared. There are several stages in the process of determining user habits, such as cleaning data, changing unstructured data to structured, then going through the rule association stage using an a priori algorithm applied to social media data so that the relationship can be seen between words. After that, machine learning is applied so that comparisons occur in seeing the relationships between words. This is new research in producing a model to identify user behavior using association rules and machine learning so that it can be used to map positive, negative or neutral user behavior

Optimizing Opportunistic Routing between Communities Using Decision Trees and Apriori Algorithm

This study proposes a novel data transmission framework aimed at optimizing node data transmission between different communities. We have designed an intelligent routing strategy based on node characteristics for situations where the source node and destination node have different communities. Through the decision tree algorithm, we trained a model to identify "messenger nodes" that are specifically responsible for cross community data transmission. In addition, the association rule model constructed using the apriori algorithm can calculate the probability of establishing a connection between the "messenger node" and the destination community node, thereby selecting the optimal path for data transmission. This method effectively reduces transmission delay and improves the efficiency and reliability of data transmission.

Research on the Reform and Development of Ideological and Political Theory Classes for College Students in the Context of Practical Parenting

Abstract In the context of practical education, deepening the reform of quality education and implementing the fundamental task of cultivating moral integrity have become the guidelines and the basis for the development of curriculum reform in colleges and universities. This study conducts data mining based on the association rule model on the influencing factors of the reform of the Civic and Political Theory Course in colleges and universities to obtain the key influencing factors in the process of the reform of the theory course. Then, under the perspective of deep learning, the idea of practical parenting is integrated, and the structural framework of Civic and Political Theory course reform is designed from four directions: learning theme, learning objectives, learning activities, and evaluation feedback, and finally, the course reform method of this paper is examined through teaching practice. The results show that the influence weights of self-factors, school education factors, family factors, and social factors are 0.3022, 0.3521, 0.1381, and 0.2076, respectively, with self-factors and school education as the key influencing factors. After the teaching practice, the P-value of each dimension of the students under the curriculum reform based on this paper is less than 0.05, and the grade of the Civics and Political Science Theory course is improved from 81.47 to 90.45, which is an increase of 7.49 points compared with the class of the regular course. The reform method based on deep learning in this study can promote students’ development and improve their academic performance, which provides a reference for the research on the reform and development of the Civics and Political Science Theory course.

Research on machine learning based processing strategies for large-scale datasets

Abstract In this paper, we first mine the interconnections between data in large-scale datasets through association rule models in machine learning and then perform T -time K-Means clustering on the mined datasets to realize large-scale data integration. On this basis, a classification prediction model based on an enhanced ChebNet model is proposed, which combines the efficient feature extraction capability of graph convolutional neural network and the accurate prediction advantage of big data analysis to effectively realize the processing of large-scale data sets. Taking the tobacco production monitoring data as an example, the model performs well in predicting the correlation of cigarette sensory indexes, especially when the sliding window size is 30 and the prediction jump step is 1. The model performance reaches the optimal, which provides strong support for the quality control of cigarette production, and is capable of processing large-scale datasets of tobacco production.

The Role and Strategy of Big Data in Promoting Local Tourism Development

Abstract As science and technology advance, the tourism industry is increasingly pivoting towards digital and intelligent transformation. The pivotal role of big data in shaping tourism trends is becoming more apparent. This study leverages big data to explore its impact on tourism development and proposes a big data-based model for this purpose. An influence mechanism model tailored to local tourism’s unique characteristics was developed to guide this analysis. Employing data mining techniques, this research utilizes the association rule model to forecast local tourism trends. Additionally, it applies heat evaluation and hotspot analysis models to examine local tourism activities. Spatial characteristics of local tourism are investigated using the nearest neighbor algorithm. An empirical study conducted in Province H illustrates these concepts in action. Here, the entertainment and leisure segment aligns most closely with the overall clustering characteristics, achieving a Z-score of -200.01. The peak hotness months in Province H are May and October, with hotness indices of 0.0438 and 0.0468, respectively. Entertainment and leisure activities make the most significant contribution to this metric, with an average value of 0.0235. Moreover, the prediction error does not exceed 5%, underscoring the validity and practical utility of this study’s methodological approach in analyzing local tourism development. This provides a robust scientific basis for harnessing data to foster local tourism growth.

A Combination of Association Rules and Optimization Model to Solve Scheduling Problems in an Unstable Production Environment

Production problems have a significant impact on the on-time delivery of orders, resulting in deviations from planned scenarios. Therefore, it is crucial to predict interruptions during scheduling and to find optimal production sequencing solutions. This paper introduces a selflearning framework that integrates association rules and optimisation techniques to develop a scheduling algorithm capable of learning from past production experiences and anticipating future problems. Association rules identify factors that hinder the production process, while optimisation techniques use mathematical models to optimise the sequence of tasks and minimise execution time. In addition, association rules establish correlations between production parameters and success rates, allowing corrective factors for production quantity to be calculated based on confidence values and success rates. The proposed solution demonstrates robustness and flexibility, providing efficient solutions for Flow-Shop and Job-Shop scheduling problems with reduced calculation times. The article includes two Flow-Shop and Job-Shop examples where the framework is applied.

Research on the interaction between university physical culture education and college students’ physical exercise in the context of big data

Abstract The background of big data has developed deeply, the application of the field has been broadened, and the value of data has been vigorously manifested. In order to study the interaction between physical culture education and exercise in universities in this era, this paper uses Clementine 12.0 data mining software to build a data mining model of association rules of university physical culture education courses and mine the course feature vectors. Based on the mining results, we designed the second classroom physical culture education courses with different physical culture characteristics. Constructing a scoring method and rating scale for the effectiveness of physical exercise among college students, and the physical exercise index scores are obtained through fuzzy operations. Finally, the interaction between physical culture education and exercise in universities in this context is analyzed according to the relationship between physical culture education courses and physical exercise performance. After the physical culture education course began, the physical exercise intensity score of the experimental group of first-year college girls increased by 8%, the physical exercise time score increased by 10%, the physical exercise frequency score increased by 15.2%, and the total physical exercise score increased by 7% after the physical culture education course. This shows that university physical culture education is positively correlated with college students’ physical activity, and campus physical culture has a significant predictive effect on students’ subjective performance of physical activity behavior. Optimizing university physical culture education not only improves students’ physical quality and promotes the development of their physical and mental health but also provides a reference for strengthening students’ physical education.

A graph structure feature-based framework for the pattern recognition of the operational states of integrated energy systems

The recognition of operational state patterns is crucial for the safe, reliable and profitable operational management of integrated energy systems (IES). However, considering that the monitored operational data are often not labelled, supervised machine learning classification methods cannot be directly applied. In this study, based on the idea of graph theory, a framework of interpretable time series pattern recognition methods based on graph structural features is proposed. Firstly, the time series data is segmented into local subsequences by a shapelet-based time series classification method. Secondly, time-graphlets are constructed by means of a visibility graph algorithm and an original graph structure feature-based representation method is proposed. Then, pattern recognition of the different operational states of IES is performed in the form of multidimensional feature vectors. Thirdly, an association rule model is designed to analyze the supply and demand fluctuations, price fluctuations and couplings between multiple energy subsystems, based on which the pattern recognition results are annotated to match the operational characteristics of the IES. Fourthly, a shapelet-based state transfer network graph is constructed, and the complex network centrality metrics are used to analyze the operating characteristics and key operating states of the system over time. The new method is applied for pattern recognition of operational states in a Spanish IES. The results show that the proposed method can increase the interpretability of the recognition results and provide explanatory labels for the operational data. Furthermore, the vulnerable operational states of the system can be revealed, which allows managers to monitor the operational state of the system and take early defensive measures against the more threatening operational patterns. This suggests that the methodological framework presented in this paper is a promising approach to pattern recognition that can provide interpretable annotations for unsupervised operational data of IES and their safe management.

Intelligent Voice System Design for Optimizing E-Business Advertising Rhetoric Based on SVM Algorithm.

With the emergence and development of artificial intelligence, design is no longer a process that can be completed only by creativity and knowledge. This article introduces research based on voice data mining and further discusses the optimization and application of rhetorical methods for e-commerce advertising. This article is working from two main aspects: one is to use association rule models in speech data mining to obtain useful rule relationships between Chinese prosody parameters, and the other is to use neural networks and data items in speech data mining. Using computer technology can make online advertisements interact with users. In addition to clicking on advertisements to jump to product pages, online advertisements also have multichannel sensory feelings, which can provide rich information and stimulate interest through interaction with users. We need to optimize the rhetoric of the advertisement and pay attention to the combination of the beauty of speech and the content of the advertisement when designing the advertisement. At the same time, we must also pay attention to the accuracy and elegance of the advertising language and wording.

Discussion on Redundant Processing Algorithm of Association Rules Based on Hypergraph in Data Mining

With the rapid advancement of big data, it is becoming a great problem for people to find objective information in the database. The relevance data processing rule for digging the information can be the way. Relevance data processing rule for digging the information is mainly studied in three aspects: data dimension, data abstraction level, and processing variable type. In the aspect of rules, the research mainly focuses on three aspects: active relationships, passive rules, and uncommon relationship rules. Association rules of digging the data can be the most well-employed investigation goal and aim for the data digging. Along with the advancement of the information scale, the time rate of traditional relationship rules exploration of counting ways is too low. How to increase the time rate for the way of counting is the main research content of relevance data processing rules for digging the information. Current relevance data processing rules for digging the information have two limitations: (1) metrics such as support, confidence, and lift rely too much on expert knowledge or complex adjustment processes in value selection; (2) it is often difficult to explain rare association rules. Based on the existing research, this paper proposes a Markov logic network framework model of association rules to address the above shortcomings. The theory of a hypergraph and system is proposed, and the method of a hypergraph in 3D matrix modeling is studied. Aiming at the new characteristics of big data analysis, a new super edge definition method is introduced according to the definition of the system, which greatly enhances the ability to solve problems. In the cluster analysis and calculation of hypergraph, this paper will use the hypergraph segmentation operator hMETIS to carry out the cluster analysis method in order to achieve higher accuracy in cluster analysis and calculation. As for the test of cycle ones, which is in line with the relevance of the hypergraph with clear directions, the thesis will offer a brand-new way to make an analysis and turn the rule of relevance into the hypergraph with clear directions with a new definition of the near linking matrix, and it will change the dealing way from the test of the cycle and more ones into the linking f bricks and circles, which is a new way to explore. This paper uses two datasets of different sizes to conduct rule prediction accuracy experiments on the Markov logic network framework model algorithm of association rules and the traditional association rule algorithm. The results show that compared with the traditional association rule algorithm, the rules obtained by the Markov logic network framework model of association rules have a higher prediction accuracy.

A Parallel FP-Growth Mining Algorithm with Load Balancing Constraints for Traffic Crash Data

Traffic safety is an important part of the roadway in sustainable development. Freeway traffic crashes typically cause serious casualties and property losses, being a serious threat to public safety. Figuring out the potential correlation between various risk factors and revealing their coupling mechanisms are of effective ways to explore and identity freeway crash causes. However, the existing association rule mining algorithms still have some limitations in both efficiency and accuracy. Based on this consideration, using the freeway traffic crash data obtained from WDOT (Washington Department of Transportation), this research constructed a multi-dimensional multilevel system for traffic crash analysis. Considering the load balancing, the FP-Growth (Frequent Pattern- Growth) algorithm was optimized parallelly based on Hadoop platform, to achieve an efficient and accurate association rule mining calculation for massive amounts of traffic crash data; then, according to the results of the coupling mechanism among the crash precursors, the causes of freeway traffic crashes were identified and revealed. The results show that the parallel FPgrowth algorithm with load balancing constraints has a better operating speed than both the conventional FP-growth algorithm and parallel FP-growth algorithm towards processing big data. This improved algorithm makes full use of Hadoop cluster resources and is more suitable for large traffic crash data sets mining while retaining the original advantages of conventional association rule mining algorithm. In addition, the mining association rules model with the improvement of multi-dimensional interaction proposed in this research can catch the occurrence mechanism of freeway traffic crash with serious consequences (lower support degree probably) accurately and efficiently.

Relationship Extraction and Processing for Knowledge Graph of Welding Manufacturing

Acquiring welding domain relationships and forming a knowledge graph can positively impact complex engineering problem solving and intelligent manufacturing applications. However, relationships are lacking in the welding domain. The relationship extraction and processing solution are designed to handle data with different characteristics in welding fabrication. The BiLSTM+Attention and CR-CNN models are employed to extract relations in unstructured documents. The neighborhood rough set-based association rule model is proposed for project-specific documents to accomplish relationship acquisition, in which invalid attributes are removed via neighborhood rough sets and attribute values are related via association rules. In addition, the knowledge graph is built based on extracted relationships, and unique empirical relationships are handled by introducing relational nodes and databases. The results show that BiLSTM+Attention gets a good score with Macro-average metrics (0.788 for Precision, 0.846 for Recall, and 0.816 for F1-score). The relational rules obtained via the proposed model are consistent with the production experience. The constructed knowledge graph effectively handles empirical relationships while positively impacting knowledge retrieval, intelligent question and answer, and decision-making for complex engineering problems.

Research on Intelligent Operation and Maintenance Model of Large Database for Electric Power Industry

Existing research on automated database operation and maintenance for the electrical industry mainly focuses on distributed and cloud platforms, and there is a lack of traditional large-scale database intelligent operation and maintenance research. This paper designs an overall operation and maintenance model framework of “intelligent perception-intelligent decision-intelligent execution”, and proposes feasible implementation plans, including: (1) Use the prophet time series forecasting model to perceive and predict important database performance indicators, and dynamically adjust the threshold of each performance indicator according to the predicted value; (2) Perform correlation analysis on abnormal indicators through the association rule model to construct “Indicators”->Operation” optimized combination operation strategy library for intelligent decision making; (3) According to the intelligent decision library, automatically restrict the associated operations under abnormal conditions to ensure the normal operation of the service and realize intelligent execution.

Analysis of Characteristics of Tennis Singles Matches Based on 5G and Data Mining Technology

The level of technical and tactical decision-making in a tennis game has a very important impact on the outcome of the game. How to discover the characteristics and rules of the game from a large amount of technical and tactical data, how to overcome the shortcomings of traditional statistical methods, and how to provide a scientific basis for correct decision-making are a top priority. Based on 5G and association analysis data mining theory, we established a data mining model for tennis technical offensive tactics and association rules and conducted specific case studies. It can calculate the maximization and distribution rate of certain technologies, also distinguish between the athlete’s gain and loss rate and the spatial position on the track, and use artificial statistical methods to cause errors and subjective participation. This solution provides objective and scientific decision support for this problem and is used in the decision-making of the landing point in tennis match technology and tactics. Experimental simulation shows that the data mining technology analysis system used for regional tennis matches is more concise, efficient, and accurate than traditional movie analysis methods.

Improving food safety through data pattern discovery in a sensor-based monitoring system

Increased digitalization has enabled firms to better monitor and improve operations to meet the increasing demands of food safety. Sensor-based monitoring systems are widely adopted by food producers to improve quality management and facility monitoring capabilities. However, existing studies on data mining in food safety applications are typically focussed on the development of standalone models; thereby tasking users with high levels of manual data input or requiring extensive, customer-specific post model implementation endeavour. To address the existing gap, this paper proposes a transferable association rule model that enables scalability with actionable information and data insight. The model was developed using the CRISP-DM framework with datasets of more than 4000 alarm entries from a global fast food franchise. The proposed integrated system is capable of providing reliable visual information for multiple managers to monitor their respective quality performance. This study contributes to the existing body of knowledge by providing empirical insights in the context of digitalisation and food safety.

Optimization of Teaching Management System Based on Association Rules Algorithm

The teaching management department carries all the work related to teaching in the whole school. A scientific, efficient, and complete teaching management system cannot only help the teaching management department improve work efficiency and quality but also greatly reduce many problems caused by manual labour risk. This paper designs and implements a teaching management system based on an improved association rule algorithm. First, aiming at the low efficiency of the Apriori algorithm for mining association rules, an association rule model based on interest is proposed. Second, use the MapReduce calculation model to partition the transaction database, then use the improved Apriori optimization algorithm for mining, and finally merge the mining results to obtain frequent itemsets. Through experiments, the optimized algorithm has greatly improved selection mining and computing time than traditional algorithms.

The art of the question: the structure of questions posed by youth soccer coaches during training

ABSTRACT Background From a sports perspective, pedagogical researchers have suggested questioning is an effective instructional tool and pedagogical strategy for developing critical thinking, problem-solving and decision-making skills. Questions enable coaches to contextualise athlete learning by encouraging and guiding them to identify and explore solutions to game-based problems. While previous investigations have explored the frequency of questions and provided some understanding of the interactions within a coaching session, there is still limited empirical knowledge related to the structure and profile of coach questioning strategies. Purpose This study aims to explore how youth soccer coaches, implementing the FFA National Curriculum, have used questioning techniques within specific coaching contexts/activities to promote athlete learning. Method Australian youth soccer coaches (i.e. U12 – U16; n = 19) were filmed during regular training sessions. A questioning profile coding system was developed and used to provide a detailed profile of the types of questions asked by the coaches in different coaching situations. The frequency of each coded event (i.e. type, context, who, what, knowledge, and activity) was recorded. Data Analysis Descriptive statistics (mean ± standard error) were used to describe all coded information. Association rule models were generated to determine the extent to which a questioning profile descriptor (i.e. Question Type, Knowledge, Context, Who, What) occurred in the presence of another. Results Coaches asked on average 71 questions per session (0.88 questions per minute), with slightly greater use of convergent (i.e. closed) questions (52.2%) than divergent (i.e. open) questions (47.8%). Coaches use convergent questions that are instructional and promote lower order thinking. In contrast, divergent questions are generally related to a game tactic or principle and are asked when the team has stopped an activity. The rule models indicated there is a general trend when considering the structure and profile of the type of question (i.e. convergent; divergent) asked in the different training session activity types. During drills and small-sided games, coaches asked convergent questions to the team, which were instructional in nature and required lower level knowledge. For large games, the question was more targeted to the individual while the activity was on-going, but still required lower level knowledge. Divergent questions generally related to game tactics although only 7% of these questions asked players to problem solve. Conclusion The results of the current study indicate that coaches are posing more questions within a coaching session, compared to previous studies, possibly reflecting the emphasis placed on questioning as an athlete learning strategy within the FFA National Curriculum. Association rule models provided greater insights into the distinct ‘styles’ or profiles typically implemented by coaches during specific types of activity. From a learning perspective, coaches should consider the players needs and wants when determining which type of question best suits the situation and be prepared to move between types of questions depending on player responses. Future research should consider the perspectives of coaches and athletes to determine why questions were asked, and how effective they were in reaching their intended purposes.

Risk prediction and early warning for air traffic controllers’ unsafe acts using association rule mining and random forest

In the context of big data, scientific judgment of the future trend or state of unsafe acts of air traffic controllers (ATCers) plays an important role in the prevention of unsafe incidents. Therefore, based on the previous prediction and early warning studies, this study chose the methods of correlation analysis and nonlinear modeling to scientifically predict and warn ATCers’ unsafe acts. Firstly, according to the process of association rule mining, the association rules among risk factors and controllers' unsafe acts were mined. On the basis of association rule mining, the prediction and early warning model was established by random forest algorithm. The prediction data samples were used to predict ATCers' errors and violations respectively. The results show that the number of two-dimensional association rules is the largest, followed by one-dimensional and two-dimensional association rules, and four-dimensional association rules is the least. For the prediction of errors and violations, the deviation between the predicted value and actual value is small, indicating that the prediction accuracy is high. Business ability, technical environment, inadequate supervision and mental states are of greater importance to the prediction of errors, while inadequate supervision, organizational climate, organizational process and mental states are more important for the prediction of violations. The application of association rule and random forest model can effectively predict ATCers' errors and violations, and send signals to relevant departments according to the threshold and alarm level to report dangerous situations, thus reducing the losses caused by ATCers' unsafe acts.

Association Rule Model of On-demand Lending Recommendation for University Library

University library which is connected with the Internet is more convenient to search, but the huge amount of data is not convenient for users who lack precise target. In this study, the traditional association rule algorithm was improved by Bayesian algorithm, and then simulation experiment was carried out taking borrowing records of 1000 students as examples. In order to verify the effectiveness of the improved algorithm, it was compared with the traditional association rule algorithm and collaborative filtering algorithm. The results showed that the recommendation results of the improved association rule recommendation algorithm were more relevant to students’ majors, and the coincidence degree of different students was low. In the objective evaluation of the performance of the algorithm, the accuracy, recall rate and F value showed that the personalized recommendation performance of the improved association rule algorithm was better and the improved association rule algorithm could recommend users with the book type that they need.

Research on Physical Fitness Test Data Mining and Analysis Based on Apriori Algorithm

Based on the analysis of the characteristics of Apriori algorithm, this paper uses the iterative method of layer by layer search to find out the relationship of item sets in the students’ body measurement database to form the mining model of association rules. The model maximizes the generation of candidate sets, reduces the size of frequent sets, and achieves good performance. At the same time, the average GM (1,1) model is constructed by the accumulation of the original sequence and the least square estimation of the parameters, as well as the whitening differential equation form, to predict the trend of College Students’ physique change in the future, and the research shows that the error of the model is small and the precision is high.