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This tutorial paper will survey the research on association rule-based table data analysis and its application. The proposed methods obtain helpful information from discrete table data through rules, which differs from statistical table data analysis by mean or variance. Regression expressions in continuous value table data can characterize past data and predict future data values. The same will hold for rules in discrete value table data. This tutorial paper consists of the following three parts. Part I: Background and Examples of Rule Generation from Tables Part II: Mathematical Research of Rule Generation from Tables Part III: Realization of Software Tools and Applications. We introduce the research trends in this field widely through three parts. [ABSTRACT FROM AUTHOR]

To solve the problem of anomaly detection in annular metal turning surfaces, this paper develops an anomaly detection algorithm based on a priori information and a multi-scale self-referencing template by combining the imaging characteristics of annular workpieces. First, the annular metal turning surface is unfolded into a rectangular expanded image using bilinear interpolation to facilitate subsequent algorithm development. Second, the grayscale information from the positive samples is used to obtain the a priori information, and a multi-scale self-referencing template method is used to obtain its own multi-scale information. Then, the phase error and large-size anomaly interference problems of the self-referencing method are overcome by combining the a priori information with its own information, and an accurate response to anomalous regions of various sizes is realized. Finally, the segmentation completeness of the anomalous region is improved by utilizing the region growing method. The experimental results show that the proposed method achieves a mean pixel AUROC of 0.977, and the mean M_IOU of segmentation reaches 0.788. In terms of efficiency, this method is also much more efficient than the commonly used anomaly detection algorithms. The proposed method can achieve rapid and accurate detection of defects in annular metal turning surfaces and has good industrial application value. [ABSTRACT FROM AUTHOR]

The complexity of alarm detection and diagnosis tasks often results in a lack of alarm log data. Due to the strong rule associations inherent in alarm log data, existing data augmentation algorithms cannot obtain good results for alarm log data. To address this problem, this paper introduces a new algorithm for augmenting alarm log data, termed APRGAN, which combines a generative adversarial network (GAN) with the Apriori algorithm. APRGAN generates alarm log data under the guidance of rules mined by the rule miner. Moreover, we propose a new dynamic updating mechanism to alleviate the mode collapse problem of the GAN. In addition to updating the real reference dataset used to train the discriminator in the GAN, we dynamically update the parameters and the rule set of the Apriori algorithm according to the data generated in each epoch. Through extensive experimentation on two public datasets, it is demonstrated that APRGAN surpasses other data augmentation algorithms in the domain with respect to alarm log data augmentation, as evidenced by its superior performance on metrics such as BLEU, ROUGE, and METEOR. [ABSTRACT FROM AUTHOR]

To reveal complex causes of aircraft events, this paper aims to mine association rules between the trigger probability and relative strength via a modified Apriori algorithm. Clustering is adopted for data preprocessing and TF–IDF value calculation. Causative item sets of aircraft events are obtained based on the accident causation 2–4 model and are coded to establish code indicators. By avoiding the use of statistical methodologies to resolve not-a-number (NaN) values for altering the interrelations among causes, an enhancement in the Apriori algorithm is proposed by considering frequent items. By extracting frequent patterns, in this paper, all the association rules that satisfy three perspectives (support, confidence and lift) are determined by constantly generating and pruning candidate item sets. A network graph is used to visualize the association rules between different unsafe events and all types of causes. Finally, 9835 representative pieces of data, including general unsafe events, general incidents and serious incidents from the Southwest Air Traffic Management Bureau, are selected for analysis. The results show that improper energy allocation, poor conflict resolution ability, inadequate onsite management duties, adoption of a luck mentality, and occurrence of controller oversight are highly correlated with general unsafe events, and failure to rectify incorrect recitation is notably correlated with general incidents, while inadequate manual promotion, lack of conflict judgement and insufficient safety management are strongly correlated with serious incidents. This study quantitatively reveals the potential patterns and characteristics of mutual interactions among various types of historical aircraft events and highlights directions for controllable prevention and prediction of aircraft events. [ABSTRACT FROM AUTHOR]

Subway construction is often in a complex natural and human-machine operating environment, and that complicated setting leads to subway construction being more prone to safety accidents, which can cause substantial casualties and monetary losses. Thus, it is necessary to investigate the safety risks of subway construction. The existing literature on the identification and assessment of subway construction safety risks (SCSR) is susceptible to the influence of subjective factors. Moreover, although existing studies have explored the interrelationships between different risks, these studies usually analyze the interrelationships of single risks, lack the study of risk chain transfer relationships, and fail to find out the key path of risk transfer. Therefore, this paper innovatively combines text mining, association rules, and complex networks to deep mine subway construction safety incident reports and explore the risk transfer process. Firstly, it uses text mining technology to identify subway construction safety risks. Then, association rules are introduced to explore the causal relationships among safety risks. Finally, the key safety risks and important transfer paths of subway construction safety accidents (SCSA) are obtained based on the complex network model. Research results show that (a) improper safety management, unimplemented safety subject responsibilities, violation of operation rules, non-perfect safety responsibilities system and insufficient safety education and training are the key safety risks in SCSA; (b) two shorter key risk transfer paths in the subway construction safety network can be obtained: insufficient safety education and training→lower safety awareness→violation of operation rules→safety accidents; insufficient safety checks or hidden trouble investigations→violation of operation rules→safety accidents; (c) in the process of risk transfer, the risk can be controlled by controlling the key safety risk or cutting off the transfer paths. This paper provides new ideas and methods for SCSR identification and influence element mining, and the results of the study help safety managers propose accurate subway construction safety risk control measures. [ABSTRACT FROM AUTHOR]

The existing blockchain system mostly adopts the equal mining mode. All bookkeepers (entities) record the ledgers on a single main chain, and the data storage is random. Moreover, in complex or classified financial scenarios, the data of the main chain is difficult to realize association or regular storage, resulting in low efficiency of storage and query. At the same time, in the existing blockchain system, event traceability is mostly only found in the source block, and the implicit association between entities cannot be identified, so the query has limitations. To solve these problems, this paper proposes a composite blockchain associated event tracing method. This method firstly constructs the blockchain composite chain storage structure model, proposes the concept of private chain and alliance chain, and realizes the adaptive data association storage in complex or classified scenarios. Secondly, on the basis of obtaining the event source entity block, the auxiliary storage space is established to transfer storage relevant data. A tracing method of associated entity block based on the Apriori algorithm is proposed, and then the obtained traceability entity block is constructed as the source event correlation graph, so as to describe the association relationship between the event entities. Finally, a risk assessment system based on reinforcement learning is proposed to realize the risk assessment of traceability entity. Experiments show that the composite blockchain associated event tracing method proposed in this paper can reduce 60% of the storage overhead, improve 90% of the query accuracy and 50% of the security. can reduce the storage overhead by 60%, improve the query accuracy by 90% and improve security by 50%. [ABSTRACT FROM AUTHOR]

With the rapid development of water conservancy engineering and infrastructure construction, there are many safety hazards in the construction process of water conservancy engineering, so it is of great significance to study the potential hazards in the construction process. In this context, this paper proposes a task scenario-based association mining method for hydraulic engineering hidden danger records. By analyzing transaction characteristics, the traditional Apriori algorithm is improved to optimize pruning results and generate hidden danger association rules. The research results of this paper have been successfully applied to the investigation and management of hidden dangers in the Xinmenghe dredging project. Based on the mapping of association rules driven by task scenarios, hidden dangers association rules in specific task scenarios are mined to assist construction safety managers in hidden dangers investigation, which reduces the complexity of the algorithm, reduces the running time of the algorithm and improves the efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

Understanding visitors' spatial choice behavior is important in developing effective policies to counteract overcrowdedness in attractive urban heritage areas. This research presents a comprehensive analysis of visitor location choice behavior, aiming to address two primary objectives. First, this paper investigates the relationship between visitor segments and the choice of particular Points of Interest (POIs). Second, this paper explores the impacts of visitors' experiences and visitor segments on their revisit intentions. We used a sample of 320 visitors who had been to Amsterdam within the last five years to collect data about their location choice behavior and intention to revisit after a recent visit to the city. Combining the revealed choices and intentions of pre-defined visitor segments obtained from a stated choice experiment, association rules are extracted to reveal differences in the patterns of behaviors related to the segment. The findings identify associations between various POIs, including museums such as the Rijksmuseum and Madame Tussauds, and visitor classes, which include "cultural attraction seekers", "selective sightseers", and "city-life lovers". Furthermore, binary logistic regression analysis reveals that affective experiences, such as feelings of comfort, happiness, and annoyance, have a significant influence on visitors' intentions to revisit the destination in the future. This research found that "cultural attraction seekers" and "selective sightseers" display a higher likelihood of considering a return visit to the city. [ABSTRACT FROM AUTHOR]

Because of speedy development at worldwide information several mining algorithms have been developed over the years. Apriori Algorithm is one of the most productive algorithm which is used to excerpt frequent patterns from huge database likely tera and penta bytes of data and find out the appropriate association rule for distinguish the knowledge. It basically needs two important things: minimum support and minimum confidence. Firstly, we check whether the frequent item are greater than or equal to the minimum support threshold value and we find the frequent item sets respectively. Secondly, the minimum confidence constraint is used to generate association rules according to the minimum confidence threshold value. In this paper we propose an algorithm (Apriori) used to mine the frequent patterns and association rules. The Apriori algorithm generates candidate set during each step. It abbreviates the item sets by dispose the infrequent item sets that exactly not match the minimum threshold from the candidate sets. To avoid the propagation of candidate set which is expensive the FP Growth algorithm is used to mine the item set. The FP Growth does not generate the candidate set instead it generates an optimized data set that is FP tree from the dataset. [ABSTRACT FROM AUTHOR]

Safety signal detection is an integral component of Pharmacovigilance (PhV), which is defined by the World Health Organization as "science and activities relating to the detection, assessment, understanding, and prevention of adverse effects or any other possible drug related problems". The purpose of safety signal detection is to identify new or known adverse events (AEs) resulting from the use of pharmacotherapeutic products. While post-marketing spontaneous reports from different sources are commonly utilized as a data source for detecting these signals, there are underlying challenges arising from data complexity. This paper investigates the implementation of the Apriori algorithm, a popular method in association rule mining, to identify frequently co-occurring drugs and AEs within safety data. We discuss previous applications of the Apriori algorithm for safety signal detection and conduct a detailed study of an improved method specifically tailored for this purpose. This enhanced approach refines the classical Apriori method to effectively reveal potential associations between drugs/vaccines and AEs from post-marketing safety monitoring datasets, especially when AEs are rare. Detailed comparative simulation studies across varied settings coupled with the application of the method to vaccine safety data from the Vaccine Adverse Event Reporting System (VAERS) demonstrate the efficacy of the improved approach. In conclusion, the improved Apriori algorithm is shown to be a useful screening tool for detecting rarely occurring potential safety signals from the use of drugs/vaccines using post-marketing safety data. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Nowadays, educational data mining technology has received more and more attention from scholars in China, and the application of correlation between student behavior data and student achievement to teaching management has become a hot research topic. Starting from the study of the potential association between book borrowing and student achievement in the big data environment, the paper analyzes the correlation between book borrowing and student achievement based on the Apriori algorithm and concludes that there is a strong correlation rule between book borrowing and student achievement. Based on BP neural network prediction algorithm, the paper constructs an early warning model for student performance by predicting book borrowing through course performance. The absolute value of the error between the predicted value of book borrowing and the real value of borrowing is used as a basis to make early warning for students' performance, so as to realize the monitoring of students' learning situation, thereby providing theoretical suggestions for teachers' teaching and promoting the school's management of students. [ABSTRACT FROM AUTHOR]

This paper proposes a paradigm of integration and optimization of English teaching resources based on the association rule algorithm and improves the Apriori algorithm by introducing interest measure and manual labeling through semisupervised learning of the neural network to improve the quality of English instruction assisted by the multimedia network. The efficiency of the method is higher than the original Apriori algorithm and the Apriori algorithm based on hash technology, according to experimental results. The new integration and optimization of the algorithm-based teaching model of English teaching resources also guide multimedia and network-assisted English teaching activities. [ABSTRACT FROM AUTHOR]

The Apriori algorithm and DM technology are introduced, examined, and a relationship between bettering teachers' classroom teaching quality, methods, and means is discussed. An evaluation system for student achievement in higher education is proposed and built on the basis of the Apriori algorithm. The Apriori algorithm is also improved in order to further avoid blind search during mining and increase the effectiveness of frequent itemsets. This paper avoids repeatedly scanning the database and instead reads the entire database into a two-dimensional array, which increases program performance. The algorithm is used to extract useful data from the database using data association mining in the higher education management system. According to the experimental findings, the improved Apriori algorithm's accuracy can reach 94.81%, which is 10.31% higher than the accuracy of the original Apriori algorithm. The outcomes demonstrate the applicability and reliability of the algorithm model developed in this paper. For managers in teaching and management, it can be a useful reference. [ABSTRACT FROM AUTHOR]

With the growing size of online social communities, influence propagation in social networks has become a hot topic of the research. Most of the studies in this area are based on the assumption that the structure of the social network is static and does not change during the information spread process. However, real-world social networks are dynamic. Modeling this continuous dynamic behavior of social networks is a challenge that must be tackled. This paper proposes a hybrid Genetic Network Programming with Apriori algorithm (GNPA) for Influence Maximization (IM) in social networks. Proposed GNPA is a meta-heuristic based optimization algorithm that handles the dynamicity, i.e. user attribute values or connection between users that changes with time. The working of GNPA is divided into 5 steps. It begins with the identification of the initial population of seeds using discounted degree method. Next, it predicts the future changes using the Apriori algorithm and updates the network accordingly. After updating the network dynamics, the influence score is estimated using the local consistent Factorization machines for each edge of the network. Finally, the diffusion score of each individual of the population is calculated using a linear cascade model. After the completion of all the above steps, the population is updated by replacing the weak individuals with new individuals using mutation and crossover, which is the last step of GNPA. The efficacy of GNPA is evaluated over two real and two synthetic datasets with low out-degree ratio and high out-degree ratio. Experimental results demonstrated that GNPA is able to predict the changing behavior of the users close to the actual-time network and improved the influence propagation 16% to 38% as compared to the contemporary counterparts. [ABSTRACT FROM AUTHOR]

Association rule mining is intently used for determining the frequent itemsets of transactional database; however, it is needed to consider the utility of itemsets in market behavioral applications. Apriori or FP-growth methods generate the association rules without utility factor of items. High-utility itemset mining (HUIM) is a well-known method that effectively determines the itemsets based on high-utility value and the resulting itemsets are known as high-utility itemsets. Fastest high-utility mining method (FHM) is an enhanced version of HUIM. FHM reduces the number of join operations during itemsets generation, so it is faster than HUIM. For large datasets, both methods are very expenisve. Proposed method addressed this issue by building pruning based utility co-occurrence structure (PEUCS) for elimatination of low-profit itemsets, thus, obviously it process only optimal number of high-utility itemsets, so it is called as optimal FHM (OFHM). Experimental results show that OFHM takes less computational runtime, therefore it is more efficient when compared to other existing methods for benchmarked large datasets. [ABSTRACT FROM AUTHOR]

Association rule mining (ARM) is a technique for discovering meaningful associations within databases, typically handling discrete and categorical data. Recent advancements in ARM have concentrated on refining calculations to reveal connections among various databases. The integration of shuffled frog leaping optimization (SFLO) processes has played a crucial role in this pursuit. This paper introduces an innovative SFLO-based method for performance analysis. To generate association rules, we utilize the apriori algorithm and incorporate frog encoding within the SFLO method. A key advantage of this approach is its one-time database filtering, significantly boosting efficiency in terms of CPU time and memory usage. Furthermore, we enhance the optimization process’s efficacy and precision by employing multiple measures with the modified SFLO techniques for mining such information. The proposed approach, implemented using MongoDB, underscores that our performance analysis yields notably superior outcomes compared to alternative methods. This research holds implications for fruit shape database mining, providing robust support for fruit class classification. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to develop an interface management risk interaction modeling and analysis methodology applicable to complex systems in high-speed rail construction projects, reveal the interaction mechanism of interface management risk and provide theoretical support for project managers to develop appropriate interface management risk response strategies. Design/methodology/approach: This paper introduces the association rule mining technique to improve the complex network modeling method. Taking China as an example, based on the stakeholder perspective, the risk factors and significant accident types of interface management of high-speed rail construction projects are systematically identified, and a database is established. Then, the Apriori algorithm is used to mine and analyze the strong association rules among the factors in the database, construct the complex network, and analyze its topological characteristics to reveal the interaction mechanism of the interface management risk of high-speed rail construction projects. Findings: The results show that the network is both scale-free and small-world, implying that construction accidents are not random events but rather the result of strong interactions between numerous interface management risks. Contractors, technical interfaces, mechanical equipment, and environmental factors are the primary direct causal factors of accidents, while owners and designers are essential indirect causal factors. The global importance of stakeholders such as owners, designers, and supervisors rises significantly after considering the indirect correlations between factors. This theoretically explains the need to consider the interactions between interface management risks. Originality/value: The interaction mechanism between interface management risks is unclear, which is an essential factor influencing the decision of risk response measures. This study proposes a new methodology for analyzing interface management risk response strategies that incorporate quantitative analysis methods and considers the interaction of interface management risks. [ABSTRACT FROM AUTHOR]

Digital systems are nowadays ubiquitous and often comprise an extremely high level of complexity. Guaranteeing the correct behavior of such systems has become an ever more pressing need for manufacturers. The correctness of digital systems can be addressed resorting to formal verification techniques, such as model checking. Currently, it is usually impossible to determine a priori the best algorithm to use given a verification task and, thus, portfolio approaches have become the de facto standard in model checking verification suites. This paper describes the most relevant algorithms and techniques, at the foundations of bit-level SAT-based model checking itself. [ABSTRACT FROM AUTHOR]

This paper addresses the challenging scheduling of crude oil operations (SCOO) problem, characterized by the intricate sequencing of activities involving discrete events and continuous variables. Given the NP-Hard nature of scheduling problems due to their combinatorial complexity, this study employs a data-driven optimization approach. Initially, historical operational data relevant to the SCOO are scrutinized; however, due to data limitations, small-scale instances are solved using a mathematical programming model to generate data. Subsequently, operational solution data are processed using the Apriori algorithm, a renowned data mining technique. The insights gained are translated into heuristic rules, laying the groundwork for a novel data-driven heuristic algorithm tailored for the SCOO problem. This algorithm is then applied to a 45-day scheduling scenario, demonstrating the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

The complex interaction between emissions, meteorology, and atmospheric chemistry makes accurate predictions of particulate pollution difficult. Advanced data mining techniques can reveal potential laws, providing new possibilities for understanding the evolution and causes of air pollution. Based on the Granger method and block modeling analysis, this paper explored the intercity spillover effects of hourly PM2.5 in Hubei Province, China, to determine the specific role (i.e., overflow, limited overflow, bilateral, inflow, and limited inflow) of each city on regional pollution formation. Furthermore, a dynamic Apriori algorithm considering time-lag effects was used to mine the spatio-temporal associations of extreme PM2.5 pollution events among different cities. Results suggest that the northern and central cities with high-level PM2.5 concentration in Hubei have a significant spillover effect, whereas the eastern and southern cities generally play a role as the sink of pollutants. Based on the association rules of extreme PM2.5 pollution, four main pollutant transport channels were excavated and well matched with the trajectories extracted by the atmospheric model. This paper provides new insights for exploring the interaction of intercity particulate pollution, which is a supplement and cross-validation of the model results. [ABSTRACT FROM AUTHOR]

Haze and mist caused by air quality, weather, and other factors can reduce the clarity and contrast of images captured by cameras, which limits the applications of automatic driving, satellite remote sensing, traffic monitoring, etc. Therefore, the study of image dehazing is of great significance. Most existing unsupervised image-dehazing algorithms rely on a priori knowledge and simplified atmospheric scattering models, but the physical causes of haze in the real world are complex, resulting in inaccurate atmospheric scattering models that affect the dehazing effect. Unsupervised generative adversarial networks can be used for image-dehazing algorithm research; however, due to the information inequality between haze and haze-free images, the existing bi-directional mapping domain translation model often used in unsupervised generative adversarial networks is not suitable for image-dehazing tasks, and it also does not make good use of extracted features, which results in distortion, loss of image details, and poor retention of image features in the haze-free images. To address these problems, this paper proposes an end-to-end one-sided unsupervised image-dehazing network based on a generative adversarial network that directly learns the mapping between haze and haze-free images. The proposed feature-fusion module and multi-scale skip connection based on residual network consider the loss of feature information caused by convolution operation and the fusion of different scale features, and achieve adaptive fusion between low-level features and high-level features, to better preserve the features of the original image. Meanwhile, multiple loss functions are used to train the network, where the adversarial loss ensures that the network generates more realistic images and the contrastive loss ensures a meaningful one-sided mapping from the haze image to the haze-free image, resulting in haze-free images with good quantitative metrics and visual effects. The experiments demonstrate that, compared with existing dehazing algorithms, our method achieved better quantitative metrics and better visual effects on both synthetic haze image datasets and real-world haze image datasets. [ABSTRACT FROM AUTHOR]

Traditional clustering methods fail to accurately cluster the feature vectors of backers and macth the potential backers to compatible crowdfunding projects, mainly due to their sensitivity to the setting of the initial value. In this paper, we use the Apriori algorithm in conjunction with other machine learning tools to cluster the potential backers and provide more accurate recommendations for crowdfunding projects. Focusing on potential projects listed in a major reward-based crowdfunding platform, we first train the data obtained from the available list of backers. Using the Apriori algorithm, the degree of association between different project backers is then obtained, and weight calculation of the backers is carried out according to the association degree of the backers. The degree of association is used as a key index to cluster similar backers. Finally, we test the model and determine whether clustering can correctly classify the data in the test set based on the Apriori algorithm. Our experimental results show that there is 90% accuracy, precision and recall of the model. The proposed solution outperforms the other five benchmark methods and offers an imporved matchmaking by connecting the listed crowdfunding projects to the right backers. [ABSTRACT FROM AUTHOR]

This paper extracts the causes of collusion behavior based on literature analysis and expert interviews and obtains collusion causation data. The Apriori algorithm is used to mine the relationship between the causes of collusion behavior, and the network model of the causes of collusion behavior is constructed. The successive failures theory mines the most easily evolved causation chain of collusion behavior. The study results showed that: (1) The critical causes of the formation of collusion are self-discipline consciousness and difficulty of investigation. The strong control ability of causation network of collusion behavior is self-discipline consciousness, difficulty of investigation, and transparency of rights operation. (2) Based on the analysis of the group case data, eight causation chains are most likely to form collusion in actual cases, among which the causation chain of collusion behavior that occurs frequently is "difficulty of investigation⟶self-discipline consciousness⟶interest chain". (3) In view of the causation nodes in the causation chain of collusion behavior, we propose more effective preventive and preventive control measures for collusion between bidders and tenderers in construction projects from three aspects, namely, behavior awareness binding, collusion implementation dilemma and collusion supervision deterrence. [ABSTRACT FROM AUTHOR]

With the complexity and variability of data structures and dimensions, traditional clustering algorithms face various challenges. The integration of network science and clustering has become a popular field of exploration. One of the main challenges is how to handle large-scale and complex high-dimensional data effectively. Hypergraphs can accurately represent multidimensional heterogeneous data, making them important for improving clustering performance. In this paper, we propose a hypergraph-clustering method dubbed the "high-dimensional data clustering method" based on hypergraph partitioning using an improved Apriori algorithm (HDHPA). First, the method constructs a hypergraph based on the improved Apriori association rule algorithm, where frequent itemsets existing in high-dimensional data are treated as hyperedges. Then, different frequent itemsets are mined in parallel to obtain hyperedges with corresponding ranks, avoiding the generation of redundant rules and improving mining efficiency. Next, we use the dense subgraph partition (DSP) algorithm to divide the hypergraph into multiple subclusters. Finally, we merge the subclusters through dense sub-hypergraphs to obtain the clustering results. The advantage of this method lies in its use of the hypergraph model to discretize the association between data in space, which further enhances the effectiveness and accuracy of clustering. We comprehensively compare the proposed HDHPA method with several advanced hypergraph-clustering methods using seven different types of high-dimensional datasets and then compare their running times. The results show that the clustering evaluation index values of the HDHPA method are generally superior to all other methods. The maximum ARI value can reach 0.834, an increase of 42%, and the average running time is lower than other methods. All in all, HDHPA exhibits an excellent comparable performance on multiple real networks. The research results of this paper provide an effective solution for processing and analyzing large-scale network datasets and are also conducive to broadening the application range of clustering techniques. [ABSTRACT FROM AUTHOR]

It is important to capture passengers' public transit behavior and their mobility to create profiles, which are critical for analyzing human activities, understanding the social and economic structure of cities, improving public transportation, assisting urban planning, and promoting smart cities. In this paper, we develop a generative adversarial machine learning network to characterize the temporal and spatial mobility behavior of public transit passengers, based on massive smart card data and road network data. The Apriori algorithm is extended with spatio-temporal constraints to extract frequent transit mobility patterns of individual passengers based on a reconstructed personal trip dataset. This individual-level pattern information is used to construct personalized feature vectors. For regular and frequent public transit passengers, we identify similar transit mobility groups using spatio-temporal constraints to construct a group feature vector. We develop a generative adversarial network to embed public transit mobility of passengers. The proposed model's generator consists of an auto-encoder, which extracts a low-dimensional and compact representation of passenger behavior, and a pre-trained sub-generator containing generalization features of public transit passengers. Shenzhen City is taken as the study area in this paper, and experiments were carried out based on smart card data, road network data, and bus GPS data. Clustering analysis of embedding vector representation and estimation of the top K transit destinations were conducted, verifying that the proposed method can profile passenger transit mobility in a comprehensive and compact manner. [ABSTRACT FROM AUTHOR]

With the continuous progress of society and economy, more and more attention has been paid to the physical quality of students. As an essential support carrier, physical education (PE) class is responsible for the exercise and improvement of the physical quality and fitness of students. In this paper, with physical training as a breakthrough point, the data on training quality are evaluated based on the Apriori algorithm and the factor mining is carried out accordingly. Through the Apriori algorithm, an in-depth analysis is carried out in this paper on the practical teaching status of physical training programs at colleges and universities in the country. From various perspectives such as the development of the existing physical training programs and their functional features, the advantages of physical training programs in teaching at colleges and universities are analyzed. At the same time, suggestions are given with regard to the current teaching model and status quo at colleges and universities to achieve the goal of driving the development of physical training in the country. Finally, the results of the practical case analysis indicate that physical training programs are feasible in the education curriculum at colleges and universities. To some extent, the spirit of physical training programs is similar to the concept of physical education at colleges and universities. There are many types of physical training programs, which can be used as rich resources for physical education at colleges and universities. From the results of the research, it can be seen that the Apriori algorithm can be used to evaluate the training quality of physical education effectively; the mastery levels of physical education skills among male students are generally higher than those among female students, and the difference is relatively significant; the algorithm proposed in this paper can be used to evaluate the data effectively, which is of great significance for supporting the decisions on improving the quality of training. [ABSTRACT FROM AUTHOR]

Data mining technology can analyze and mine university management data, provide more data support for university management, and play an important role in optimizing teaching quality, but these technologies are rarely applied to the work of university counselors. Based on this, this paper studies the construction and research of college counselors' innovative work mode based on data mining technology. Based on the simple analysis of the impact of big data on the traditional working mode of counselors, this paper introduces the application of data mining commonly used in colleges and universities and puts forward the existing shortcomings. The innovative working mode of college counselors is designed. In the analysis of students' daily behavior, the advantages of cluster analysis and support vector machine are used to analyze students' consumption behavior. The Apriori algorithm is applied to the student achievement early warning management to improve the Apriori algorithm. Simulation results show that the proposed algorithm can shorten the running time, reduce the number of frequent item sets, and improve the classification accuracy. [ABSTRACT FROM AUTHOR]

The integration of multimedia teaching refers to a novel teaching mode that combines multimedia, information resources, and information methods with content and teaching process in the teaching process with the guidance of contemporary educational ideas and organically unifies them on a spatial and temporal platform, so as to better accomplish the teaching tasks. However, with the deepening of network teaching and school connotation construction, various resources such as media materials, courseware, network courses, and teaching cases generated by education and teaching, as well as academic works, papers, patents, project results, and lectures of experts and professors gathered by scientific research, are increasing. In this situation, how to integrate teaching resources and courses to provide students with an independent learning platform and an information-based learning environment has become an urgent issue. For this problem, data mining has shown a strong vitality, and data mining can find out the potential connection between datasets. Association rule mining is the most researched and widely used data mining method. In this paper, we propose a multimedia network-assisted English integration of educational resources method based on association rule algorithm to give full play to the role of "multimedia technology" and realize the integration of traditional English teaching and information network culture, so as to achieve the purpose of optimizing English teaching. The experimental results show that with the increase of data volume, the average testing time of Apriori is reduced by 56.49 s compared with MapReduce, so the advantage of Apriori algorithm is more obvious. Therefore, this research can effectively provide students with rich and colorful personalized resources, realize high-performance interactive learning, and help students cultivate their independent learning ability as well as their lifelong learning habits. [ABSTRACT FROM AUTHOR]