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In business, managers may use the association information among products to define promotion and competitive strategies. The mining of high-utility association rules (HARs) from high-utility itemsets enables users to select their own weights for rules, based either on the utility or confidence values. This approach also provides more information, which can help managers to make better decisions. Some efficient methods for mining HARs have been developed in recent years. However, in some decision-support systems, users only need to mine a smallest set of HARs for efficient use. Therefore, this paper proposes a method for the efficient mining of non-redundant high-utility association rules (NR-HARs). We first build a semi-lattice of mined high-utility itemsets, and then identify closed and generator itemsets within this. Following this, an efficient algorithm is developed for generating rules from the built lattice. This new approach was verified on different types of datasets to demonstrate that it has a faster runtime and does not require more memory than existing methods. The proposed algorithm can be integrated with a variety of applications and would combine well with external systems, such as the Internet of Things (IoT) and distributed computer systems. Many companies have been applying IoT and such computing systems into their business activities, monitoring data or decision-making. The data can be sent into the system continuously through the IoT or any other information system. Selecting an appropriate and fast approach helps management to visualize customer needs as well as make more timely decisions on business strategy.

Hypertension is one of the most common cardiovascular diseases, which will cause severe complications if not treated in a timely way. Early and accurate identification of hypertension is essential to prevent the condition from deteriorating further. As a kind of complex physiological state, hypertension is hard to characterize accurately. However, most existing hypertension identification methods usually extract features only from limited aspects such as the time-frequency domain or non-linear domain. It is difficult for them to characterize hypertension patterns comprehensively, which results in limited identification performance. Furthermore, existing methods can only determine whether the subjects suffer from hypertension, but they cannot give additional useful information about the patients’ condition. For example, their classification results cannot explain why the subjects are hypertensive, which is not conducive to further analyzing the patient’s condition. To this end, this paper proposes a novel hypertension identification method by integrating classification and association rule mining. Its core idea is to exploit the association relationship among multi-dimension features to distinguish hypertensive patients from normotensive subjects. In particular, the proposed method can not only identify hypertension accurately, but also generate a set of class association rules (CARs). The CARs are proved to be able to reflect the subject’s physiological status. Experimental results based on a real dataset indicate that the proposed method outperforms two state-of-the-art methods and three common classifiers, and achieves 84.4%, 82.5% and 85.3% in terms of accuracy, precision and recall, respectively.

The issue of multimorbidity in patients with type 2 diabetes mellitus (T2DM) is extremely serious. However, the pattern of multimorbidity, including typical complications, remains unclear. This study aims to explore the current status and influencing factors of multimorbidity in T2DM, with a focus on mining frequent disease combination patterns and strong association rules. Data on 26 diseases were extracted from the electronic medical records of 5,838 hospitalized patients with type 2 diabetes. The chi-square test, Cochran-Armitage trend test, and logistic regression were used for the analysis of influential factors. Association rule mining was employed to explore frequent disease combinations and association rules across the entire population and subgroups stratified by gender, age, and BMI. Network graphs were used to visualize binary comorbidity relationships. Gender-specific differences in disease prevalence were found for 18 of the 26 diseases included in this study. The prevalence of multimorbidity was 97.8%, and it increased with age, with a higher prevalence in males ( P  < 0.05). The identified frequent disease combination patterns mainly centered around typical complications of T2DM. The most frequent binary comorbidity pattern was diabetic peripheral neuropathy (DPN) + diabetic peripheral vascular disease (DPVD) (support: 74.1%), which is a novel finding in the relationship between DPN and DPVD. The primary association rule identified was {DPVD + diabetic nephropathy (DN)}→{Hypertension}. Disease combination patterns and association rules varied across gender, age, and BMI. Comorbidity relationships became more complex in the middle and older age groups, as well as in the overweight and obese groups. The findings of this study can be used to guide clinicians in the prevention and treatment of multimorbidity in T2DM and provide possible directions for researchers to further investigate the causes and mechanisms.

Cardiovascular diseases continue to be the leading cause of mortality worldwide, claiming a significant number of lives each year. Despite the advancements in predictive models, including logistic regression, neural networks, and random forests, these techniques often lack transparency and interpretability, limiting their practical application in clinical settings. To address this challenge, this research introduces EPFHD-RARMING, an innovative approach designed to enhance the understanding and predictability of heart disease through the discovery of rare and meaningful patterns. EPFHD-RARMING utilizes rare association rule mining to uncover hidden and unexpected rules that identify critical factors contributing to heart disease. This method is particularly adept at identifying high-risk patterns in individuals who appear healthy but may develop heart disease under certain conditions, thus facilitating early intervention and preventive measures. By integrating these insights with established feature engineering techniques, EPFHD-RARMING enhances its practical utility, enabling medical professionals to proactively manage patient care and tailor interventions to individual risk profiles. This study demonstrates the effectiveness of EPFHD-RARMING in providing a deeper, actionable understanding of the complex dynamics of heart disease. The model’s ability to identify and interpret rare patterns holds significant promise for advancing medical analytics and improving patient outcomes. Moreover, the applicability of EPFHD-RARMING extends beyond the healthcare domain, offering valuable insights in various fields where the discovery of rare patterns is critical, such as finance, marketing, and cybersecurity. This study conducts a comprehensive evaluation, which demonstrates the superior performance of EPFHD-RARMING compared to traditional predictive models in identifying key factors contributing to heart disease, in terms of interestingness, explainability, and comprehensiveness of insights. The results underscore the potential of this innovative approach to revolutionize our understanding and prediction of heart disease, ultimately contributing to more effective and personalized healthcare solutions. This research emphasizes the importance of rare association rule mining in medical analytics and paves the way for future studies to explore and utilize these techniques across diverse domains.

In data mining, the classical association rule mining techniques deal with binary attributes; however, real-world data have a variety of attributes (numerical, categorical, Boolean). To deal with the variety of data attributes, the classical association rule mining technique was extended to numerical association rule mining. Initially, the concept of numerical association rule mining started with the discretization method, and later, many other methods, e.g., optimization, distribution are proposed in state-of-the-art. Different authors have presented various algorithms for each numerical association rule mining method; therefore, it is hard to select a suitable algorithm for a numerical association rule mining task. In this article, we present a systematic assessment of various numerical association rule mining methods and we provide a meta-study of thirty numerical association rule mining algorithms. We investigate how far the discretization techniques have been used in the numerical association rule mining methods.

This study delves into the effective integration of bi-directional cultural intercultural instruction with Civic and Political teaching in a higher vocational English course and analyzes it with an association rule mining algorithm. By screening the weighted candidate itemsets through the weighted support degree model, the study identifies the itemsets’ positive and negative correlation attributes and mines the association rules between English teaching and students’ bidirectional cultural interduction ability by combining with the decision support model. The article also analyzes the content of Civics and Chinese and foreign cultural elements in the English curriculum. It compares and examines the changes in students’ two-way cultural core literacy and its introduction effect. The results of the study show that after the bi-directional cultural Civics teaching in the English curriculum, students experienced significant changes in worldview, values and Chinese and foreign cultural cognition, with t-values of −4.312, −5.034, and −4.275, respectively, and p-values of less than 0.001. In addition, the students’ bi-directional cultural communication competence improved significantly, with scores ranging from 3.8 to 4.3. The study provides valuable insights into the promotion of Chinese-foreign cultural communication and the reform of higher vocational English teaching.

A prediscretisation of numerical attributes which is required by some rule learning algorithms is a source of inefficiencies. This paper describes new rule tuning steps that aim to recover lost information in the discretisation and new pruning techniques that may further reduce the size of rule models and improve their accuracy. The proposed QCBA method was initially developed to postprocess quantitative attributes in models generated by Classification based on associations (CBA) algorithm, but it can also be applied to the results of other rule learning approaches. We demonstrate the effectiveness on the postprocessing of models generated by five association rule classification algorithms (CBA, CMAR, CPAR, IDS, SBRL) and two first-order logic rule learners (FOIL2 and PRM). Benchmarks on 22 datasets from the UCI repository show smaller size and the overall best predictive performance for FOIL2+QCBA compared to all seven baselines. Postoptimised CBA models have a better predictive performance compared to the state-of-the-art rule learner CORELS in this benchmark. The article contains an ablation study for the individual postprocessing steps and a scalability analysis on the KDD’99 Anomaly detection dataset.

The World Health Organization has recognized road traffic accidents as a global crisis, particularly in urban environments. Despite extensive research endeavors, significant gaps remain in our understanding of how various factors interact to influence urban road traffic incidents. This study analyzed data from 4285 urban road traffic accidents in Hubei Province, employing a two-step clustering algorithm to classify accidents into distinct groups based on specific conditions. Subsequently, association rule mining was utilized to discern relationships between accident characteristics within each cluster. Additionally, a classification based on the association rule algorithm was implemented to develop a predictive model for analyzing factors contributing to casualties. The data were categorized into clusters based on weather and road conditions, with separate discussions conducted for each scenario. The findings indicated that urban congestion is the most critical factor contributing to accidents. Interestingly, rather than in severe weather, accidents were more prevalent during cloudy, light-rain conditions. Electric vehicles and motorcycles emerged as the most vulnerable groups. Furthermore, a notable interaction was observed between the day of the week, time of day, and weather conditions. The predictive model achieved an impressive average accuracy of 86.9%. This methodology facilitates the identification of contributing factors and mechanisms underlying urban road traffic accidents in China and holds potential for establishing accident analysis models in similar contexts. The interactive visualization of association rules further enhances the applicability of the findings. The findings of this study can provide valuable insights for traffic management authorities to understand the causes of urban road traffic accidents, assisting them in devising effective policy measures and countermeasures to reduce the occurrence of accidents and casualties.

Recent technical innovations and developments in computer-based technology have enabled bioscience researchers to acquire comprehensive datasets and identify unique parameters within experimental datasets. However, field researchers may face the challenge that datasets exhibit few associations among any measurement results (e.g., from analytical instruments, phenotype observations as well as field environmental data), and may contain non-numerical, qualitative parameters, which make statistical analyses difficult. Here, we propose an advanced analysis scheme that combines two machine learning steps to mine association rules between non-numerical parameters. The aim of this analysis is to identify relationships between variables and enable the visualization of association rules from data of samples collected in the field, which have less correlations between genetic, physical, and non-numerical qualitative parameters. The analysis scheme presented here may increase the potential to identify important characteristics of big datasets.

Numerical Association Rule Mining (NARM), which simultaneously handles both numerical and categorical attributes, is a powerful approach for uncovering meaningful associations in heterogeneous datasets. However, designing effective NARM solutions is a complex task involving multiple sequential steps, such as data preprocessing, algorithm selection, hyper-parameter tuning, and the definition of rule quality metrics, which together form a complete processing pipeline. In this paper, we introduce NiaAutoARM, a novel Automated Machine Learning (AutoML) framework that leverages stochastic population-based metaheuristics to automatically construct full association rule mining pipelines. Extensive experimental evaluation on ten benchmark datasets demonstrated that NiaAutoARM consistently identifies high-quality pipelines, improving both rule accuracy and interpretability compared to baseline configurations. Furthermore, NiaAutoARM achieves superior or comparable performance to the state-of-the-art VARDE algorithm while offering greater flexibility and automation. These results highlight the framework’s practical value for automating NARM tasks, reducing the need for manual tuning, and enabling broader adoption of association rule mining in real-world applications.