Search Results (38)

This study introduces a data-driven framework for enhancing the sustainable management of fish species in Romania’s Natura 2000 protected areas through ecosystem modeling and association rule mining (ARM). Drawing on seven years of ecological monitoring data for 13 fish species of ecological and socio-economic importance, we apply the FP-Growth algorithm to extract high-confidence co-occurrence patterns among 19 codified conservation measures. By encoding expert habitat assessments into binary transactions, the analysis revealed 44 robust association rules, highlighting interdependent management actions that collectively improve species resilience and habitat conditions. These results provide actionable insights for integrated, evidence-based conservation planning. The approach demonstrates the interpretability, scalability, and practical relevance of ARM in biodiversity management, offering a replicable method for supporting adaptive ecological decision making across complex protected area networks. Full article

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. Full article

In this paper, a novel mathematical formalization of Crisp-Set Qualitative Comparative Analysis (csQCA) that enables a rigorous connection with a specific class of association rule mining (ARM) problems is proposed. Although these two methodologies are frequently used to identify logical patterns in binary datasets, they originate from different traditions. While csQCA is rooted in set theory and Boolean logic and is primarily applied in the social sciences to model causal complexity, ARM originates from data mining and is widely used to discover frequent co-occurrences among items. In this study, we establish a formal mathematical equivalence between csQCA configurations and a subclass of association rules, including both positive and negative conditions. Moreover, we propose a minimization procedure for association rules that mirrors the Quine–McCluskey reduction method employed in csQCA. We demonstrate the consistency of the results obtained using both methodologies through two examples (a small-N study on internet shutdowns in Sub-Saharan Africa and a large-N analysis of immigration attitudes in Europe) and some numerical experiments. However, it is also clear that ARM offers improved scalability and robustness in high-dimensional contexts. Overall, these findings provide researchers with valuable theoretical and practical guidance when choosing between these approaches in qualitative data analysis. Full article

Association Rule Mining (ARM) relies on concept lattices as an effective knowledge representation structure. However, classical ARM methods face significant limitations, including the generation of misleading rules during data-to-formal-context mapping and poor handling of heterogeneous data types such as linguistic, continuous, and imprecise data. This study aims to address these limitations by introducing a novel fuzzy data structure called the “fuzzy iceberg lattice” and its corresponding construction algorithm. The primary objectives of this study are to enhance the efficiency of extracting and visualizing frequent fuzzy closed item sets and to optimize both execution time and storage requirements. The necessity of this research stems from the high computational cost and redundancy associated with traditional fuzzy approaches, which, while capable of managing quantitative and imprecise data, are often impractical for large-scale applications in real scenarios. The proposed approach incorporates a ‘fuzzy min-max basis algorithm’ to derive exact and approximate rule bases from the extracted fuzzy closed item sets, eliminating redundancy while preserving valuable insights. Experimental results on benchmark datasets demonstrate that the proposed fuzzy iceberg lattice outperforms traditional fuzzy concept lattices, achieving an average reduction of 74.75% in execution time and 70.53% in memory usage. This efficiency gain, coupled with the lattice’s ability to handle crisp, quantitative, fuzzy, and heterogeneous data types, underscores its potential to advance ARM by yielding a manageable number of high-quality fuzzy concepts and rules. Full article

In the iron and steel industry, evaluating the energy utilization efficiency (EUE) and determining the optimal energy matching mode play an important role in addressing increasing energy depletion and environmental problems. Electric Arc Furnace (EAF) steelmaking is a typical short crude steel production route, which is characterized by an energy-intensive fast smelting rhythm and diversified raw charge structure. In this paper, the energy model of the EAF steelmaking process is established to conduct an energy analysis and EUE evaluation. An association rule mining (ARM) strategy for guiding the EAF production process based on data cleaning, feature selection, and an association rule (AR) algorithm was proposed, and the effectiveness of this strategy was verified. The unsupervised algorithm Auto-Encoder (AE) was adopted to detect and eliminate abnormal data, complete data cleaning, and ensure data quality and accuracy. The AE model performs best when the number of nodes in the hidden layer is 18. The feature selection determines 10 factors such as the hot metal (HM) ratio and HM temperature as important data features to simplify the model structure. According to different ratios and temperatures of the HM, combined with k-means clustering and an AR algorithm, the optimal operation process for the EUE in the EAF steelmaking under different smelting modes is proposed. The results indicated that under the conditions of a low HM ratio and low HM temperature, the EUE is best when the power consumption in the second stage ranges between 4853 kWh and 7520 kWh, the oxygen consumption in the second stage ranges between 1816 m³ and 1961 m³, and the natural gas consumption ranges between 156 m³ and 196 m³. Conversely, under the conditions of a high HM ratio and high HM temperature, the EUE tends to decrease, and the EUE is best when the furnace wall oxygen consumption ranges between 4732 m³ and 5670 m³, and the oxygen consumption in the second stage ranges between 1561 m³ and 1871 m³. By comparison, under different smelting modes, the smelting scheme obtained by the ARM has an obvious effect on the improvement of the EUE. With a high EUE, the improvement of the A2B1 smelting mode is the most obvious, from 24.7% to 53%. This study is expected to provide technical ideas for energy conservation and emission reduction in the EAF steelmaking process in the future. Full article

Despite increasing industrial advancements, fatal and severe accidents, such as “Falls”, “Struck-by”, “Hit by an object”, “Be crushed”, and “Caught-in/between” accidents, persist in developed countries, including Korea. Various methods, including risk assessment, monitoring systems, technology improvements, and safety education, are being implemented to reduce accidents. However, only a few studies have revealed the causes of accidents and their interrelationships; these studies are based on limited data. Korea recently published accident data using national statistical systems, including the construction safety management integrated information (CSI), enabling the analyses of major accident types. Here, we selected various representative accident cases to minimize the duplication of the data published from 2019 to 2023 and applied the Material, Method, Machine, or Man (4M) analysis method, a risk assessment technique, to perform an accident-type-based association rule mining (ARM) analysis of the accident factors. Through the ARM analysis, we quantitatively identified complex correlations for major accidents. Based on the 4M factors derived through this analysis, we improved a 2–4 model for accident causation and proposed safety management measures for each construction entity. Full article

Introduction: Type 2 diabetes mellitus is a major global health concern, but interpreting machine learning models for diagnosis remains challenging. This study investigates combining association rule mining with advanced natural language processing to improve both diagnostic accuracy and interpretability. This novel approach has not been explored before in using pretrained transformers for diabetes classification on tabular data. Methods: The study used the Pima Indians Diabetes dataset to investigate Type 2 diabetes mellitus. Python and Jupyter Notebook were employed for analysis, with the NiaARM framework for association rule mining. LightGBM and the dalex package were used for performance comparison and feature importance analysis, respectively. SHAP was used for local interpretability. OpenAI GPT version 3.5 was utilized for outcome prediction and interpretation. The source code is available on GitHub. Results: NiaARM generated 350 rules to predict diabetes. LightGBM performed better than the GPT-based model. A comparison of GPT and NiaARM rules showed disparities, prompting a similarity score analysis. LightGBM’s decision making leaned heavily on glucose, age, and BMI, as highlighted in feature importance rankings. Beeswarm plots demonstrated how feature values correlate with their influence on diagnosis outcomes. Discussion: Combining association rule mining with GPT for Type 2 diabetes mellitus classification yields limited effectiveness. Enhancements like preprocessing and hyperparameter tuning are required. Interpretation challenges and GPT’s dependency on provided rules indicate the necessity for prompt engineering and similarity score methods. Variations in feature importance rankings underscore the complexity of T2DM. Concerns regarding GPT’s reliability emphasize the importance of iterative approaches for improving prediction accuracy. Full article

This study aimed to examine comorbidity associations across age groups of inpatients with endocrine diseases as the primary diagnosis throughout the life cycle to develop an effective management strategy. Data were obtained from the Korean National Hospital Discharge In-depth Injury Survey (KNHDS) from 2006 to 2021, involving 68,515 discharged patients aged ≥ 19 years with a principal diagnosis of endocrine disease. A database was constructed for analysis, extracting general characteristics and comorbidities. Employing R version 4.2.3, the Chi-squared test and the Apriori algorithm of ARM (association rule mining) were used for analyzing general characteristics and comorbidity associations. There were more women (53.1%) than men (46.9%) (p < 0.001, with women (61.2 ± 17.2) having a higher average age than men (58.6 ± 58.6) (p < 0.001). Common comorbidities include unspecified diabetes mellitus; essential (primary) hypertension; unspecified diabetes mellitus; and other disorders of fluid, electrolyte, and acid-base balance. Notably, type 2 diabetes mellitus, disorders of lipoprotein metabolism and other lipidemia, polyneuropathy in diseases classified elsewhere, retinal disorders in diseases classified elsewhere, and essential (primary) hypertension prevail across all age groups. Association rules further highlight specific comorbidities appearing selectively in certain age groups. In conclusion, establishing a management strategy for comorbidities in patients with a primary diagnosis of an endocrine disorder is necessary. Full article

Urban traffic accidents pose significant challenges to the sustainability of transportation infrastructure not only in Vietnam but also all over the world. To decrease the frequency of accidents, it is crucial to analyze accident data to determine the relationship between accidents and causes, especially for serious accidents. This study suggests an integrated approach using Geographic Information System (GIS) and Data Mining methods to investigate the features of urban traffic accidents in Hanoi, Vietnam aiming to solve these challenges and enhance the safety and efficiency of urban transportation. Firstly, the dataset was segmented into homogenous clusters using the two-step cluster method. Secondly, the correlation between causes and traffic accidents was examined on the overall dataset as well as on each cluster using the association rule mining (ARM) technique. Finally, the location of accident groups and high-frequency sites of accidents (hotspots) were determined by using GIS techniques. As a result, a five-cluster model was created, which corresponded to five common accident groupings in Hanoi. Moreover, the results of the study also identified the types of accidents, the main causes, the time, and the surrounding areas corresponding to each accident group. In detail, cluster 5 depicted accidents on streets, provincial, and national roads caused by motorbikes making up the highest percentage within the groups, accounting for 29.2%. Speeding and driving in the wrong lane in the afternoon and at night were the main causes in this cluster ($C_f$ ≥ 0.9 and $L_t$ ≥ 1.22). Next, cluster 2 had the second-highest proportion. Cluster 2 presented accidents between a truck/car and a motorbike on national and provincial roads, accounting for 27.8%. Cluster 1 presented accidents between a truck/car and a motorbike on local streets, accounting for 22%. Cluster 3 illustrated accidents between two motorbikes on the country lanes, accounting for 12.3%. Finally, cluster 4 depicted single-vehicle motorbike crashes, with the lowest rate of 8.8%. More importantly, this study also recommended using repeatability criteria for the same type of accidents or causes to determine the location of hotspots. Also, suggestions for improving traffic infrastructure sustainability were proposed. To our knowledge, this is the first time in which these three methods are applied simultaneously for analyzing traffic accidents. Full article

Online health communities (OHC) consist of individuals with shared health-related interests who exchange health-related information among themselves and for the benefit of others. Unfortunately, a notable issue within these communities is the dissemination of a substantial volume of inaccurate health information by various online health groups. Nevertheless, a dearth of research examining the impact of information-seeking activities within OHCs exists. This study aimed to examine the influence of direct and indirect health information-seeking behaviors, specifically among users diagnosed with Type 2 diabetes who have reported complications in OHC, also called claims. Employing association rule mining (ARM) techniques, user data from PatientsLikeMe were extracted to capture information on users’ reported complications subsequent to being diagnosed with Type 2 diabetes (N = 6371). Subsequently, we utilized zero-inflated negative binomial regression (ZINB) to evaluate the effect of direct and indirect information search activities on false notes, including their interaction of them. The outcomes of this investigation have the potential to offer patients valuable insights regarding the reliability and trustworthiness of information derived from OHCs. Full article

The frequency of work accidents in construction projects is relatively high. One contributing factor to work accidents is unsafe behavior by workers at construction sites. In Indonesia, this is the first study to investigate 2503 instances of unsafe behavior that occurred across Indonesian construction projects in relation to their attributes to obtain insightful knowledge by using the association rule mining (ARM) method. Association rule mining was used to explore the database. As a result, two consolidated rules were obtained. The most frequent unsafe behaviors were workers putting tools and materials in random places, workers not attaching safety lines at provided places, and workers moving work tools and materials in ways that were not in accordance with procedures. These unsafe behaviors were associated with accident types of falling, and being struck or cut by items, as well as violations of Manpower and Transmigration Ministerial Regulation 01/1980, and Manpower Ministerial Regulation 09/2016. The ARM results were evaluated with a reliability evaluation method before being validated by construction safety experts. Hence, the findings are reliable to be used as guideline information for safety trainers to prioritize related safety trainings and for safety inspectors when carrying out inspections on construction sites. As a result, safety management and safety performance can increase significantly. Full article

Association rule mining (ARM) is one of the most important tasks in data mining. In recent years, swarm intelligence algorithms have been effectively applied to ARM, and the main challenge has been to achieve a balance between search efficiency and the quality of the mined rules. As a novel swarm intelligence algorithm, the water wave optimization (WWO) algorithm has been widely used for combinatorial optimization problems, with the disadvantage that it tends to fall into local optimum solutions and converges slowly. In this paper, a novel hybrid ARM method based on WWO with Levy flight (LWWO) is proposed. The proposed method improves the solution of WWO by expanding the search space through Levy flight while effectively increasing the search speed. In addition, this paper employs the hybrid strategy to enhance the diversity of the population in order to obtain the global optimal solution. Moreover, the proposed ARM method does not generate frequent items, unlike traditional algorithms (e.g., Apriori), thus reducing the computational overhead and saving memory space, which increases its applicability in real-world business cases. Experiment results show that the performance of the proposed hybrid algorithms is significantly better than that of the WWO and LWWO in terms of quality and number of mined rules. Full article

(1) Background: Multimorbidity has become one of the key issues in the public health sector. This study aims to explore the patterns and health-ecological factors of multimorbidity in China to propose policy recommendations for the management of chronic diseases in the elderly. (2) Methods: A multi-stage random sampling method was used to conduct a questionnaire survey on 3637 older adults aged 60 and older in Shanxi, China. Association rule mining analysis (ARM) and network analysis were applied to analyze the patterns of multimorbidity. The health-ecological model was adopted to explore the potential associated factors of multimorbidity in a multidimensional perspective. A hierarchical multiple logistic model was employed to investigate the association strengths reflected by adjusted odds ratios and 95% confidence. (3) Results: Multimorbidity occurred in 20.95% of the respondents. The graph of network analysis showed that there were 6 combinations of chronic diseases with strong association strengths and 14 with moderate association strengths. The results of the ARM were similar to the network analysis; six dyadic chronic disease combinations and six triadic ones were obtained. Hierarchical multiple logistic regression indicated that innate personal traits (age, history of genetics, and body mass index), behavioral lifestyle (physical activity levels and medication adherence), interpersonal network (marital status), and socioeconomic status (educational level) were the common predictors of multimorbidity for older adults, among which, having no family history was found to be a relative determinant as a protective factor for multimorbidity after controlling the other covariates. (4) Conclusions: multimorbidity was prevalent in older adults and most disease combinations are associated with hypertension, followed by diabetes. This shows that diabetes and hypertension have a high prevalence among older adults and have a wide range of associations with other chronic diseases. Exploring the patterns and associated factors of multimorbidity will help the country prevent complications and avoid the unnecessary use of the health service, adopting an integrated approach to managing multimorbidity rather than an individual disease-specific approach and implementing different strategies according to the location of residence. Full article

Background: Idiopathic cardiomyopathy (ICM) is a rare disease affecting numerous physiological and biomolecular systems with multimorbidity. However, due to the small sample size of uncommon diseases, the whole spectrum of chronic disease co-occurrence, especially in developing nations, has not yet been investigated. To grasp the multimorbidity pattern, we aimed to present a multidimensional model for ICM and differences among age groups. Methods: Hospital discharge records were collected from a rare disease centre of ICM inpatients (n = 1036) over 10 years (2012 to 2021) for this retrospective analysis. One-to-one matched controls were also included. First, by looking at the first three digits of the ICD-10 code, we concentrated on chronic illnesses with a prevalence of more than 1%. The ICM and control inpatients had a total of 71 and 69 chronic illnesses, respectively. Second, to evaluate the multimorbidity pattern in both groups, we built age-specific cosine-index-based multimorbidity networks. Third, the associated rule mining (ARM) assessed the comorbidities with heart failure for ICM, specifically. Results: The comorbidity burden of ICM was 78% larger than that of the controls. All ages were affected by the burden, although those over 50 years old had more intense interactions. Moreover, in terms of disease connectivity, central, hub, and authority diseases were concentrated in the metabolic, musculoskeletal and connective tissue, genitourinary, eye and adnexa, respiratory, and digestive systems. According to the age-specific connection, the impaired coagulation function was required for raising attention (e.g., autoimmune-attacked digestive and musculoskeletal system disorders) in young adult groups (ICM patients aged 20–49 years). For the middle-aged (50–60 years) and older (≥70 years) groups, malignant neoplasm and circulatory issues were the main confrontable problems. Finally, according to the result of ARM, the comorbidities and comorbidity patterns of heart failure include diabetes mellitus and metabolic disorder, sleeping disorder, renal failure, liver, and circulatory diseases. Conclusions: The main cause of the comorbid load is aging. The ICM comorbidities were concentrated in the circulatory, metabolic, musculoskeletal and connective tissue, genitourinary, eye and adnexa, respiratory, and digestive systems. The network-based approach optimizes the integrated care of patients with ICM and advances our understanding of multimorbidity associated with the disease. Full article

Apartment housing occupies the highest proportion of the domestic construction market and significantly influences the flow of the real estate market. Frequent design changes and reconstruction in new apartment housing projects lead to an increase in construction cost and schedule, and a decline in design and construction quality, which is an important issue affecting the quality of use for occupants. The causal relationship of design changes and error in new apartment building projects has not been previously identified. Accordingly, design changes management activities in the construction phase using reactive manner are a critical risk that causes the productivity of the project to deteriorate. In this study, a complex and non-linear causal relationship between the design change factors was investigated using the association rule mining technique (ARM), a type of data mining technique. In particular, the associated relationship between design change factors that can be changed according to conditions that significantly affect the productivity and performance of projects, such as a contractor’s ranking in the field, contract price which means the project size, and contractor selection methods, was identified. The association rule between the design changes at the construction phase derived in this research can be used as a guide to identify and minimize the risk of design changes in advance. Full article