Multidimensional Association Rules Research Articles

The user satisfaction evaluation of MOOC teaching platform based on multidimensional association rules

The user satisfaction evaluation of MOOC teaching platform based on multidimensional association rules

The user satisfaction evaluation of MOOC teaching platform based on multidimensional association rules

The user satisfaction evaluation of MOOC teaching platform based on multidimensional association rules

Calibrating subjective data biases and model predictive uncertainties in machine learning-based thermal perception predictions

Heating, Ventilation, and Air Conditioning (HVAC) systems in large-scale buildings often struggle to ensure satisfactory thermal comfort for diverse occupants while minimizing energy waste. Achieving this goal requires developing reliable prediction models that capture the changing and varied occupant thermal perceptions in different spaces. Despite their widespread use, many machine learning (ML) based prediction models suffer from subjective data biases and model predictive uncertainties, causing inaccurate estimation for occupant needs and leading to suboptimal building controls. The authors propose a data-model integration method that identifies and calibrates the inherent uncertainties of existing ML models in both data and model dimensions, ensuring reliable thermal perception predictions. This method introduces the Multidimensional Association Rule Mining (M-ARM) algorithm to identify biased human responses by exploring interrelationships among four perception metrics: thermal sensation, comfort, acceptability, and preference. Our method reveals significant performance enhancements in seven ML models, enhancing the F1-score by up to 5.53%. By leveraging reliability diagrams and Expected Calibration Error (ECE) scores, we also expose the models’ vulnerability to miscalibration and the need for calibrated predictions. We further evaluate six calibration techniques (e.g., Platt Scaling and Isotonic Calibration) on these models and uncover their potential to enhance prediction reliability performance, highlighting a reduction of up to 80.66% in ECE scores. The authors also investigated the impacts of dataset sizes, classifiers, and calibration methods on the proposed method. Our research offers insight into creating robust data-driven strategies for thermal perception predictions, ultimately contributing to optimized occupant comfort and energy efficiency in buildings.

Representation, mining and analysis of unsafe behaviour based on pan-scene data.

To describe the safety rules of various industrial process data and explore the characteristics of unsafe behaviour, the association rules of unsafe behaviour based on pan-scene were proposed in this study. First, based on the scene data theory, unsafe behaviour was described by eight dimensions (time, location, behavioural individual, unsafe action, behavioural attribute, behavioural trace, professional category and risk level) to achieve scene data description and structural transformation. Second, the Apriori algorithm was used to explore the distribution rules of unsafe behaviour dimensions and the interaction between different dimensions from two perspectives: single-dimensional statistical analysis and multidimensional association rule mining. Finally, through SPSS Modeler software, an empirical analysis of pan-scene data for subway construction was conducted, and the association rules between type of work, construction stage, working time and unsafe action were identified. Some strong association rules were produced by the association analysis. For example, during the 13:00–17:00 of the excavation floor stage, the most frequent unsafe action of machine operators is the irregular binding of lifting objects. This result could explain why some unsafe actions are prone to occur in different construction stages and working times for workers of different types, which can be controlled and managed in a targeted manner, thus reducing the possibility of accidents.

Automatic Integration Algorithm of Vocal Performance Learning Materials Based on Multidimensional Association Rules

As computer and multimedia technology advances and as the variety of knowledge display forms expands, there are more ways for people to obtain knowledge and information. The best course in musicology is voice performance. A crucial issue is how to select and categorize valuable curriculum materials of varying quality levels. This paper implements an automatic classification and integration algorithm for vocal performance learning materials using machine learning technology and tests it on the corresponding dataset, beginning with multidimensional association rule mining technology and the premise that multimedia data contain numerous useful characteristics. Experiments demonstrate the classification precision and data integration capacity of the proposed algorithm. Vocal performance is the most engaging course in musicology. To cultivate corresponding talents, we can rely on college and university offline courses and the rapidly developing multimedia technology to train talents online. An important topic is how to efficiently select and classify curriculum materials of varying quality in order to provide them to students with different learning needs. We can achieve the automatic classification of the course content by leveraging the superior learning capabilities of artificial intelligence and machine learning technology. Consequently, this paper implements an automatic classification and integration algorithm for vocal performance learning materials using machine learning-related technology and conducts an experimental test on the corresponding dataset, beginning with multidimensional association rule mining technology and the perspective that multimedia information itself contains a large number of useful characteristics. Experiments demonstrate that the proposed algorithm has a high level of classification accuracy and data integration capability.

Construction of Correlation Analysis Model of College Students' Sports Performance Based on Convolutional Neural Network.

This paper proposes a network model recurrent fully connected network (RFC-Net) based on recurrent full convolution and polarization change. RFC-Net enriches the network by reconstructing and fine-tuning the fully convolutional network and adding recurrent convolutions to it. By studying the data mining technology of multidimensional association rules, based on the existing algorithms, this paper improves the shortcomings of the algorithms and realizes an efficient and practical method for data mining based on interdimensional multidimensional association rules. On the basis of mastering the actual student information, the effectiveness of the method is tested, and an employment analysis system based on association rules is established. Aiming at the fact that traditional grade prediction methods ignore the different influences of different behavioral characteristics on grades, and considering that behavioral data in different periods have different influences on student grades, the grade prediction problem is abstracted into a time series classification problem. The mechanism is combined with long short-term memory neural network to construct a performance prediction model based on Attention-BiLSTM. Experiments show that the prediction model proposed in this paper improves the accuracy and effectively improves the prediction quality compared with the logistic regression model with a better prediction effect in the traditional benchmark model and the long short-term memory neural network model without the introduction of the attention mechanism. Research shows that physical performance and academic performance are not contradictory. We must face up to the status of physical exercise in schools; as long as physical exercise is properly arranged, it can inspire students to form a spirit of unity, interaction, positivity, and perseverance in cultural studies.

Research on Personalized Recommendation of Higher Education Resources Based on Multidimensional Association Rules

The personalized recommendation method of higher education resources currently cannot carry out multidimensional association analysis of learners, situations, and resources and cannot extract accurate resources for learners, resulting in a large error. This study constructs a personalized recommendation method for higher education resources based on multidimensional association rules. This algorithm clarifies the multidimensional association rules, extracts the key data from massive educational resources, and groups the same kind of data by using a frequent itemset algorithm for mining association rules, namely, the Apriori algorithm. Combined with traditional data mining technology, this study constructs a new personalized recommendation model for education resources based on multidimensional association rules, which achieves the accurate extraction of higher education resources and ensures the matching degree between learners and resources. The experimental results show that the personalized recommendation model of educational resources in this study effectively makes up for the disadvantages of the traditional data mining algorithms, with a small root mean square error and short data mining time, within 20 ms.

Condensed Representations of Association Rules in n-ary Relations

The problem of association rules mining has given rise to a rich literature, especially in classic binary bidimensional data. In particular, the representation of the set of rules without loss of information is well understood. This is not the case in multidimensional binary data. This paper shows that the knowledge of n-1 components of every closed n-sets of a multidimensional Boolean tensor, as well as the cardinality of the remaining dimension, is enough to allow for the derivation of the confidence of every multidimensional association rule. This generalises well-known results in the bidimensional case. This paper provides experimental comparisons between the numbers of closed n-sets and frequent associations.

Optimization of Multi-Dimensional Association Rule Mining Algorithm Studies the Spatial Form of Cultural Landscape Heritage

The research on the spatial form of cultural landscape heritage is an important link in the protection of cultural landscape heritage. The optimization of the research by using multidimensional association rules algorithm can improve the efficiency of the research on the spatial form of cultural landscape heritage. This paper first explains the development and classification of the concept of cultural landscape and the concept and classification of data mining, and discusses the optimization of multi-dimensional association rule mining algorithm to study the spatial form of cultural landscape heritage, so as to provide readers with reference.

Research and Application of a Multidimensional Association Rules Mining Method Based on OLAP

As to the problems of low data mining efficiency, less dimensionality, and low accuracy of traditional multidimensional association rules in the university big data environment, an OLAP-based multi-dimensional association rule mining method is proposed, which combines hash function and marked transaction compression technology to solve the problem of excessive or redundant candidate sets in the Apriori algorithm, and uses On Line Analytical Processing to manage the intermediate data in the association mining process , in order to reduce the time overhead caused by repeated calculations. To verify the validity of the proposed method, a learning situation analysis system is constructed in the field of colleges and universities. The multi-dimensional association rules mining method is used to analyze more than 21,000 desensitized real data, in order to mine the key factors affecting students' academic performance. The experimental results show that the proposed multi-dimensional mining model has good mining results and significantly improves the time performance.

Research on human factors cause chain of ship accidents based on multidimensional association rules

In order to probe the mechanism of human causation factors in ship navigation accidents effectively, the Reason-SHEL model was introduced to establish human causation factors index system meanwhile the multidimensional association rule algorithm was applied to obtain the chains of human causation factors. Firstly, the human factors index system is constructed based on the Reason-SHEL model. Secondly, the established human factors index system is used to analysis the cause of the accidents, and the initial accident data collection is obtained. The Reason model combined with classical association rules algorithm to design multidimensional association rule algorithm, then the designed algorithm and C# programming language is used to mine accident frequent itemsets of human factors until the larger frequent itemset can't be found. Then we get the human factors cause chain of ship accidents. The case study shows that the above research method is effective. The above research methods provide new theoretical and technical means for excavating the cause chain of ship accidents and preventing ship traffic accidents.

Detection of the Association Rules of the Occurrence of Brucellosis in Humans Using Spatial Data Mining

Background and Objectives: Brucellosis is the most common disease in humans and animals, where its distribution is roughly global, so the control of this disease has an important impact on human health and livestock industry. The most important causes of this disease are environmental parameters (location). In this regard, identifying and discovering the relationship between environmental parameters and the outbreak and incidence of the disease can be effective in controlling and providing preventive measures and thus reducing the cost of treatment for humans and animals. Material and Methods: In this research, using spatial data and multidimensional association rules, the association between the occurrence of Brucellosis with environmental and human parameters such as temperature, humidity, altitude, land use, occupation and history of contact with the livestock and use of non-pasteurized dairy has been investigated with emphasis on location. The statistical population of this study is people with brucellosis in Hamedan province in 1394. Results: The results of this study indicate that factors of location and non location included temperature, altitude, humidity, history used of non-pasteurized dairy, history of contact with infected animals sick people, job and land use, can be effective in the spread of the disease. Conclusion: The results of the study show that the factors of temperature and elevation have had more effects in the discovered laws, so that areas with higher temperatures and lower altitudes than other areas include more patients. Also, people with agricultural and livestock jobs occupy the highest rates of patients in the laws that were discovered.

Abnormal Event Health-Status Monitoring Based on Multi-Dimensional and Multi-Level Association Rules Constraints in Nursing Information System

Since the traditional methods such as statistical analysis are used to analyze the long-term nursing service strategy more, and no association rule method is used to solve the problems encountered in the development of long-term nursing service. This paper aims to provide some data support for the establishment of a scientific and reasonable long-term nursing information system by synthesizing the basic national conditions and drawing lessons from foreign experience. Therefore, in order to improve the analytical accuracy of multidimensional multi-model data, minimal rule which includes rules with single item as the consequent and the minimal number of items as the antecedent, can be introduced to derive the same decisions as other association rules without information loss, while the number of minimal rules is much less than of all rules. From these association rules, it can be known that diabetes has a strong association with coronary heart disease, hypertension, fatty liver, chronic arterial occlusive disease, abnormal lipid metabolism and other diseases; according to the analysis of the antecedent and consequence in the results table of the above association rules, it can be found that diabetes is highly likely to cause hypertension, coronary heart disease, abnormal lipid metabolism, fatty liver, and there is a close relationship between various complications.

Research on power regulation service data transmission path optimisation method

Owing to the large amount of transmission data and transmission paths between equipment and applications in the power grid control centre, it is easy to cause problems such as packet loss and incomplete data transmission. An improved Apriori algorithm is proposed to mine multi-dimensional data association rules when multiple service devices are running in parallel, then optimise the discontinuous data path transmission based on the ant colony algorithm. The ant colony migration probability is used to calculate the pheromone concentration value of the ant on the discontinuous data path in a fixed period, plan the ant travel path, use the direction factor to adjust the discontinuous data transmission path appropriately, determine the optimal path, and realise the mining optimisation. Simulation results show that the proposed method can effectively improve the convergence speed of discontinuous data transmission, reduce the delay time of the data transmission network, and ensure the integrity of data transmission.

A Hybrid Pre-Post Constraint-Based Framework for Discovering Multi-Dimensional Association Rules Using Ontologies

Association rule mining is a very useful knowledge discovery technique to identify co-occurrence patterns in transactional data sets. In this article, the authors proposed an ontology-based framework to discover multi-dimensional association rules at different levels of a given ontology on user defined pre-processing constraints which may be identified using, 1) a hierarchy discovered in datasets; 2) the dimensions of those datasets; or 3) the features of each dimension. The proposed framework has post-processing constraints to drill down or roll up based on the rule level, making it possible to check the validity of the discovered rules in terms of support and confidence rule validity measures without re-applying association rule mining algorithms. The authors conducted several preliminary experiments to test the framework using the Titanic dataset by identifying the association rules after pre- and post-constraints are applied. The results have shown that the framework can be practically applied for rule pruning and discovering novel association rules.

Big data-informed energy efficiency assessment of China industry sectors based on K-means clustering

Abstract The regional energy management body has a large amount of regional industrial companies’ energy consumption data. It can evaluate the energy utilization of listed regional industrial companies based on the total data and, then, find the key points for understanding the resources usage patterns, identifying the problematic companies, and establishing good energy consumption practices. This paper reviews the research progress on big data analysis and industrial energy efficiency evaluation and focuses on the energy efficiency evaluation methods based on energy consumption process analysis and big data mining approach. Based on K-means and multi-dimensional association rules algorithm, to analyze the characteristics of regional energy consumption in different industries and companies, we cluster single industry in K-means and finding their levels of water and energy consumption. This classification provided us a reference point to identify the industries and companies to focus on and locate the bad consumption practices and environmental performance. Then, multi-dimensional association rules are used to find the correlation of processes, companies and energy efficiency to guide the energy conservation in regional energy monitor. The output of our research is a working Big Data analytics platform and the results generated from advance analytics techniques applied specifically to solve regional energy efficiency problems.

Finding main causes of elevator accidents via multi-dimensional association rule in edge computing environment

In order to discover the main causes of elevator group accidents in edge computing environment, a multi-dimensional data model of elevator accident data is established by using data cube technology, proposing and implementing a method by combining classical Apriori algorithm with the model, digging out frequent items of elevator accident data to explore the main reasons for the occurrence of elevator accidents. In addition, a collaborative edge model of elevator accidents is set to achieve data sharing, making it possible to check the detail of each cause to confirm the causes of elevator accidents. Lastly the association rules are applied to find the law of elevator Accidents.

A Sub Chunk-Confusion Based Privacy Protection Mechanism for Association Rules in Cloud Services

In cloud computing services, according to the customized privacy protection policy by the tenant and the sub chunk-confusion based on privacy protection technology, we can partition the tenant’s data into many chunks and confuse the relationships among chunks, which makes the attacker cannot infer tenant’s information by simply combining attributes. But it still has security issues. For example, with the amount of data growing, there may be a few hidden association rules among some attributes of the data chunks. Through these rules, it is possible to get some of the privacy information of the tenant. To address this issue, the paper proposes a privacy protection mechanism based on chunk-confusion privacy protection technology for association rules. The mechanism can detect unidimensional and multidimensional attributes association rules, hide them by adding fake data, re-chunking and re-grouping, and then ensure the privacy of tenant’s data. In addition, this mechanism also provides evaluation formulas. They filter detected association rules, remove the invalid and improve system performance. They also evaluate the effect of privacy protection. The experimental evaluation proves that the mechanism proposed in this paper can better protect the data privacy of tenant and has feasibility and practicality in real world applications.

Association rule mining algorithm DB-growth based on relational database

Excavating potential multidimensional valuable association rules from big data has wide application. The main association rule mining algorithm Apriori has the bottlenecks of scanning repeatedly database and generating big number of candidate sets, though the FP algorithm does not generate candidate sets, but FP-tree cannot handle the problem of storage and traversal of big data. In addition, Apriori and FP-growth algorithm needs to reconstruct association rules while implementing increment mining, its not available for growth-oriented data mining. Facing those problems, designing DB-growth algorithm based on relational database table SourceIndex, applying string combinate to generate pattern, insert or update database to construct frequent sets, mining association rules by querying database. In addition, it supports increment mining and depth mining.

Discovering descriptive rules in relational dynamic graphs

Graph mining methods have become quite popular and a timely challenge is to discover dynamic properties in evolving graphs or networks. We consider the so-called relational dynamic oriented graphs that can be encoded as n-ary relations with n ≥ 3 and thus represented by Boolean tensors. Two dimensions are used to encode the graph adjacency matrices and at least one other denotes time. We design the pattern domain of multi-dimensional association rules, i.e., non trivial extensions of the popular association rules that may involve subsets of any dimensions in their antecedents and their consequents. First, we design new objective interestingness measures for such rules and it leads to different approaches for measuring the rule confidence. Second, we must compute collections of a priori interesting rules. It is considered here as a post-processing of the closed patterns that can be extracted efficiently from Boolean tensors. We propose optimizations to support both rule extraction scalability and non redundancy. We illustrate the added-value of this new data mining task to discover patterns from a real-life relational dynamic graph.