Combines Association Rule Research Articles

Fault diagnosis method of Bayesian network based on association rules

When the number of samples is large, the scale of the Bayesian network (BN) structure search space increases exponentially with the number of nodes, resulting in a sharp increase in the difficulty of learning the BN structure. Aiming at this problem, this paper proposes a fault diagnosis model construction method combining association rules and a BN network. The Euclidean distance under the Symbolic Aggregation Approximation (SAX) algorithm is utilized to compute and average the distance between the standard and faulty samples and filter the candidate nodes by the average value, which in turn reduces the search sample space. The method of combining Association Rules algorithm with traditional BN structure learning results is used to solve the problem of wrong edges in structure learning. Finally, the maximum likelihood estimation method is used for parameter learning to complete the construction of the diagnostic network. The experimental results show that the running time of the Bayesian Network based on the Association Rules (AR-BN) model proposed in this paper is short and that the Hamming distance with the original structure is small, so this model can effectively reduce the search space and solve the problem of wrong edges, and it also has a good performance in fault diagnosis.

A machine learning-based data-driven method for risk analysis of marine accidents

In view of the frequent occurrence of marine accidents and the complex interaction of various risk-influencing factors (RIFs), a data-driven method to risk analysis that combines association rule mining (ARM) and complex network (CN) analysis is proposed in this study. The efficient FP-Growth algorithm is applied to facilitate ARM to examine risk patterns that frequently occur in marine accidents. Subsequently, CN theory is employed to scrutinise the multifaceted role of various RIFs and their interactions in the complex marine accident system, which involves the basic characteristics of the network, the identification of key RIFs through the application of the weighted LeaderRank (WLR) algorithm, and a robustness analysis. The results of the study indicate that compared with random networks, marine accident networks exhibit a higher level of complexity, which brings challenges to safety prevention and control. Inadequate regulation, violations, and deficiencies in safety management systems are identified as key RIFs, stressing the urgency of improving supervision, strengthening law enforcement and strengthening the safety management system. This study may facilitate maritime safety management of maritime traffic and the development of risk analysis methods.

A self-learning framework combining association rules and mathematical models to solve production scheduling programs

ABSTRACT Data-driven production scheduling and control systems are essential for manufacturing organisations to quickly adjust to the demand for a wide range of bespoke products, often within short lead times. This paper presents a self-learning framework that combines association rules and optimization techniques to create data-driven production scheduling. A new approach to predicting interruptions in the production process through association rules was implemented, using a mathematical model to sequence production activities in real or near real-time. The framework was tested in a case study of a ceramics manufacturer, updating confidence values by comparing planned values to actual values recorded during production control. It also sets a production corrective factor based on confidence value and success rate to avoid product shortages. The results were generated in just 1.25 seconds, resulting in a makespan reduction of 9% and 6% compared to two heuristics based on First-In-First-Out and Short Processing Time strategies.

Using Generative AI to Improve the Performance and Interpretability of Rule-Based Diagnosis of Type 2 Diabetes Mellitus

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.

Detecting potential cooperative network for tourist attractions in a destination using search data.

This study addresses the critical need for regional tourism integration and sustainable development by identifying cooperation opportunities among tourist attractions within a region. We introduce a novel methodology that combines association rule mining with complex network analysis and utilizes search index data as a dynamic and contemporary data source to reveal cooperative patterns among tourist attractions. Our approach delineates a potential cooperative network within the destination ecosystem, categorizing tourist attractions into three distinct communities: core, intermediary, and periphery. These communities correspond to high, medium, and low tourist demand scales, respectively. The study uncovers a self-organizing network structure, driven by congruences in internal tourist demand and variances in external tourist experiences. Functionally, there is a directed continuum of cooperation prospects among these communities. The core community, characterized by significant tourist demand, acts as a catalyst, boosting demand for other attractions. The intermediary community, central in the network, links the core and periphery, enhancing cooperative ties and influence. Peripheral attractions, representing latent growth areas within the destination matrix, benefit from associations with the core and intermediary communities. Our findings provide vital insights into the dynamics, systemic characteristics, and fundamental mechanisms of potential cooperation networks among tourist attractions. They enable tourism management organizations to employ our analytical framework for real-time monitoring of tourism demand and flow trends. Additionally, the study guides the macro-control of tourism flows based on the tourism network, thereby improving the tourist experience and promoting coordinated development among inter-regional tourist attractions.

Association rules combined fuzzy decision quality control technology in intelligent manufacturing

In the context of the rapid development of intelligent manufacturing, effective quality control has become the key to improving manufacturing efficiency and product quality. Traditional quality control methods are often inadequate when faced with complex production data and changing manufacturing environments. Therefore, exploring new intelligent quality control technologies to cope with these challenges in intelligent manufacturing has become an important research direction. In view of this, the study proposed a quality control technology that combines association rules and fuzzy decision-making. Firstly, association rule mining methods are used to analyze production data and extract the relationships between key quality factors. Secondly, based on these association rules, fuzzy decision technology is used to adjust and optimize the production process, ultimately achieving quality control of products in the intelligent manufacturing production process. The data showed that when running on the training set and validation set, the research method reached a stable state at 18 and 46 iterations of the system, respectively, with a minimum cost loss function value. In both batch production lines, the detection efficiency under the operation of the research method remained at 2200 units per minute. During the process of repeating the system for 6 times, the research method consistently achieved maximum control accuracy and minimum time consumption. The satisfaction of the four experts with the operation of the research method in small batch production and large batch production was 9.32 and 9.2, respectively, significantly higher than other algorithms. The above results indicated that the proposed method can effectively improve the efficiency of product quality control, reduce production costs, and ultimately reduce the rate of defective products in the production process.

Cultivation of Core Literacy of Physical Education Professionals in Private Colleges and Universities Based on the Background of Data Mining

Abstract The purpose of this paper is to analyze the main types of core literacy of sports professionals in colleges and universities using association rules and the XGBoost algorithm. Combined with the confidence rule in association analysis, it finds out the minimum support degree of all the core qualities that satisfy the core qualities of sports professionals in colleges and universities. The efficiency of modeling talent demand can be improved by using the Apriori algorithm. Adding new regression trees sequentially based on the original model using the XGBoost algorithm. The college sports talent demand model was constructed by combining association rules and the XGBoost algorithm. Based on the results, composite talents, professional talents, and application talents are the main types of sports professional talent cultivation positioning. The cultivation ratio of sports composite talents in University A is 0.7, and the cultivation ratio of application composite talents is 0.6. The school’s requirements for sports talent’s ability are the highest, resulting in a cultivation ratio of 0.75 for sports talent. This study promotes, to a certain extent, the cultivation of sports professionals in colleges and universities.

A hybrid machine learning model combining association rule mining and classification algorithms to predict differentiated thyroid cancer recurrence.

Differentiated thyroid cancer (DTC) is the most prevalent endocrine malignancy with a recurrence rate of about 20%, necessitating better predictive methods for patient management. This study aims to create a relational classification model to predict DTC recurrence by integrating clinical, pathological, and follow-up data. The balanced dataset comprises 550 DTC samples collected over 15 years, featuring 13 clinicopathological variables. To address the class imbalance in recurrence status, the Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTE-NC) was utilized. A hybrid model combining classification algorithms with association rule mining was developed. Two relational classification approaches, regularized class association rules (RCAR) and classification based on association rules (CBAR), were implemented. Binomial logistic regression analyzed independent predictors of recurrence. Model performance was assessed through accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. The RCAR model demonstrated superior performance over the CBAR model, achieving accuracy, sensitivity, and F1 score of 96.7%, 93.1%, and 96.7%, respectively. Association rules highlighted that papillary pathology with an incomplete response strongly predicted recurrence. The combination of incomplete response and lymphadenopathy was also a significant predictor. Conversely, the absence of adenopathy and complete response to treatment were linked to freedom from recurrence. Incomplete structural response was identified as a critical predictor of recurrence risk, even with other low-recurrence conditions. This study introduces a robust and interpretable predictive model that enhances personalized medicine in thyroid cancer care. The model effectively identifies high-risk individuals, allowing for tailored follow-up strategies that could improve patient outcomes and optimize resource allocation in DTC management.

Pattern analysis of auto parts failures in the after-sales service network; an interconnected approach of association rules mining and Bayesian networks in the automotive industry

PurposeWarranty-based big data analysis has attracted a great deal of attention because of its key capabilities and role in improving product quality while minimizing costs. Information and details about particular parts (components) repair and replacement during the warranty term, usually stored in the after-sales service database, can be used to solve problems in a variety of sectors. Due to the small number of studies related to the complete analysis of parts failure patterns in the automotive industry in the literature, this paper focuses on discovering and assessing the impact of lesser-studied factors on the failure of auto parts in the warranty period from the after-sales data of an automotive manufacturer.Design/methodology/approachThe interconnected method used in this study for analyzing failure patterns is formed by combining association rules (AR) mining and Bayesian networks (BNs).FindingsThis research utilized AR analysis to extract valuable information from warranty data, exploring the relationship between component failure, time and location. Additionally, BNs were employed to investigate other potential factors influencing component failure, which could not be identified using Association Rules alone. This approach provided a more comprehensive evaluation of the data and valuable insights for decision-making in relevant industries.Originality/valueThis study's findings are believed to be practical in achieving a better dissection and providing a comprehensive package that can be utilized to increase component quality and overcome cross-sectional solutions. The integration of these methods allowed for a wider exploration of potential factors influencing component failure, enhancing the validity and depth of the research findings.

Coupled risk analysis of hospital infection: A multimethod-fusion model combining association rules with complex networks

During the initial phase of COVID-19 pandemic, hospital infection (HI) accidents occurred with alarming frequency. Proactively preventing and controlling HI risks is particularly challenging as hospital is a complex and dynamic scene that involves multiple uncertainties and coupled risks. Thus, this paper develops a multimethod fusion model that combines association rule mining with complex networks to systematically analyze coupled HI risks. Firstly, risk factors of HI are obtained and categorized based on real data in hospitals. An improved Apriori algorithm is used to mine coupled relations between risk factors and generate association rules. Then, complex networks of HI risk factors are constructed based on existing rules. Through an in-depth analysis of the networks’ topology and statistics properties, this study reveals that HI risk networks follow a power-law distribution, attesting to the non-random nature of risks. The proposed approach is applied to analyze HI risks during COVID-19 outbreak in a tertiary hospital in Wuhan. Results show that the method can successfully reveal the coupling of risks and identify key risks of HI. Effective responses can be undertaken in advance to prevent identified HI risks, thereby providing valuable guidance for future hospital management.

Risk Analysis of Laboratory Fire Accidents in Chinese Universities by Combining Association Rule Learning and Fuzzy Bayesian Networks

Targeting the challenges in the risk analysis of laboratory fire accidents, particularly considering fire accidents in Chinese universities, an integrated approach is proposed with the combination of association rule learning, a Bayesian network (BN), and fuzzy set theory in this study. The proposed approach has the main advantages of deriving conditional probabilities of BN nodes based on historical accident data and association rules (ARs) and making good use of expert elicitation by using an augmented fuzzy set method. In the proposed approach, prior probabilities of the cause nodes are determined based on expert elicitation with the help of an augmented fuzzy set method. The augmented fuzzy set method enables the effective aggregation of expert opinions and helps to reduce subjective bias in expert elicitations. Additionally, an AR algorithm is applied to determine the probabilistic dependency between the BN nodes based on the historical accident data of Chinese universities and further derive conditional probability tables. Finally, the developed fuzzy Bayesian network (FBN) model was employed to identify critical causal factors with respect to laboratory fire accidents in Chinese universities. The obtained results show that H4 (bad safety awareness), O1 (improper storage of hazardous chemicals), E1 (environment with hazardous materials), and M4 (inadequate safety checks) are the four most critical factors inducing laboratory fire accidents.

Risk prediction and early warning of pilots’ unsafe behaviors using association rule mining and system dynamics

Risk prediction of pilots' unsafe behaviors is of great significance for preventing unsafe aviation incidents. However, there is a lack of effective and precise approach of dealing with this problem. This study proposes a hybrid approach of combining association rule mining (ARM) method and system dynamics (SD) model to predict and warn pilots' unsafe behaviors. Firstly, the risk factors are identified and classified according to the historical incident data by the human factor analysis and classification system. Then, the association rules between risk factors and unsafe behaviors are obtained by ARM. Finally, the SD is adopted to construct the risk prediction and early warning model for pilots' unsafe behaviors, and the applicability and effectiveness of the model are verified by the actual data from 2016 to 2020. The results of ARM show that there are 48 risk factors affecting pilots' unsafe behaviors, and 142 key association rules are formed between these risk factors and unsafe behaviors; environmental influences, pilots' adverse states, and organizational influences are all strongly related to pilots' unsafe behaviors, and the impact of environmental influences on unsafe behaviors mainly rely on the interaction with other factors. The results of SD demonstrate that although both of the cognitive error risk and the decision error risk show an increasing trend, the former increases slowly and keeps at the level of no alarm while the latter is growing faster and its risk may increase from no alarm to critical alarm; the operation error risk and the violation risk both show a downward trend, and finally remain at the no alarm level; the risk of pilots' unsafe behaviors has a stable fluctuation trend, and the risk values of most time are in the threshold ranges of major warning and critical warning. This work provides theoretical guidance for the decision-makers to develop measures to reduce incidents caused by pilots’ unsafe behaviors.

Identification of Environmental Pollutants in Construction Site Monitoring Using Association Rule Mining and Ontology-Based Reasoning

Pollutants from construction activities of building projects can have serious negative impacts on the natural environment and human health. Carrying out monitoring of environmental pollutants during the construction period can effectively mitigate environmental problems caused by construction activities and achieve sustainable development of the construction industry. However, the current environmental monitoring method relying only on various sensors is relatively singlar which is unable to cope with a complex on-site environment We propose a mechanism for environmental pollutants identification combining association rule mining and ontology-based reasoning and using random forest algorithm to improve the accuracy of identification. Firstly, the ontology model of environmental pollutants monitoring indicator in the construction site is built in order to integrate and share the relative knowledge. Secondly, the improved Apriori algorithm with added subjective and objective constraints is used for association rule mining among environmental pollutants monitoring indicators, and the random forest algorithm is applied to further filter the strong association rules. Finally, the ontology database and rule database are loaded into a Jena reasoning machine for inference to establish an identification mechanism of environmental pollutants. The results of running on a real estate development project in Jiangning District, Nanjing, prove that this identification mechanism can effectively tap the potential knowledge in the field of environmental pollutants monitoring, explore the relationship between environmental pollutants monitoring indicators and then overcome the shortcomings of traditional monitoring methods that only rely on sensors to provide new ideas and methods for making intelligent decisions on environmental pollutants in a construction site.

Data-driven decision support tool for production planning: a framework combining association rules and simulation

Nowadays, guaranteeing the highest product variety in the shortest delivery time represents one of the main challenges for most of industries. The dynamic contexts where they have to compete push them to quickly readapt their processes, increasing the need for reactive decision-support tools to identify targeted actions to improve performance. Starting from the analysis of existing decision-support tools separately adopting simulation or data mining techniques, a framework that combines Association Rule Mining (ARM) and simulation has been developed to capitalize on the benefits brought by both techniques. On the one hand, ARM supports companies in identifying the main criticalities that slow down production processes, such as different causes of stoppage, giving a priority ranking of interventions. On the other hand, data-driven simulation is used to validate the ARM results and to conduct scenario analyses to compare the KPIs values resulting from different configurations of the production processes. Once the best-impacting mitigating actions have been implemented, the proposed framework can be iteratively used to define an updated set of intervention areas to enhance, promoting continuous improvement. This data-driven approach represents the key value of the framework, guaranteeing its easy-to-readapt and iteratively application. Theoretical contributions refer to the use of simulation with ARM not only to validate relations but to perform scenario analyses in an iterative way, as well as to the novelty application in a low-tech sector. From a practical point of view, a case study in the fashion industry demonstrates the usability and reliability of the proposed framework.

Multivariate analysis of roadway multi-fatality crashes using association rules mining and rules graph structures: A case study in China.

Roadway multi-fatality crashes have always been a vital issue for traffic safety. This study aims to explore the contributory factors and interdependent characteristics of multi-fatality crashes using a novel framework combining association rules mining and rules graph structures. A case study is conducted using data from 1068 severe fatal crashes in China from 2015 to 2020, and 1452 interesting rules are generated using an association rule mining approach. Several modular rules graph structures are constructed based on graph theory to reflect the interactions and patterns between different variables. The results indicate that multi-fatality crashes are highly associated with improper operations, passenger overload, fewer lanes, mountainous terrain, and run-off-the-road crashes, representing the key variables of factors concerning driver, vehicle, road, environment, and accident, respectively. Furthermore, crashes involving different severity levels, road categories, and terrain are verified to possess unique association rules and independent crash patterns. Moreover, the proportion of severe crashes caused by a combination of human-vehicle-road-environment factors (43%) is much higher than that of normal crashes (3%). This study reveals that the hidden associations between various factors contribute to the overrepresentation and severity of multi-fatality crashes. It also demonstrates that the crash mechanisms involving multi-fatality crashes and their interactions are more complex at the system level than those for normal crashes. The proposed framework can effectively map the intrinsic link between multiple crash factors and potential risks, providing transportation agencies with helpful insights for targeted safety measures and preventive strategies.

Application of Association Rule Mining in offshore HVAC transmission topology optimization

This work develops a hybrid optimization method for determining optimal radial transmission topologies for the connection of offshore wind farms combining Association Rule Mining (ARM) and a greedy algorithm. The method is capable of optimally placing offshore substations and accounts for Capital Expenditures (CAPEX), Corrective Maintenance (CM), losses and Expected Energy Not Transmitted (EENT). The stochastic nature of wind is also considered. First, an inequality based apriori algorithm is applied to a randomly generated population of Offshore Wind Power Plant (OWPP) pairs within a specified search domain. This way, a set of simple constraints is obtained reducing the effective combinatorial search space. A verified optimal greedy algorithm is then applied to efficiently search the reduced search space for the lowest cost radial topology to connect offshore wind. The hybrid approach is shown to introduce minimal error given a sufficient sample population while greatly extending the feasible problem size of the greedy search algorithm.

Artificial intelligence-based decision support model for new drug development planning

New drug development guarantees a very high return on success, but the success rate is extremely low. Pharmaceutical companies have attempted to use various strategies to increase the success rate of drug development, but this goal has been difficult to achieve. In this study, we developed a model that can guide effective decision-making at the planning stage of new drug development by leveraging machine learning. The Drug Development Recommendation (DDR) model, we present here, is a hybrid model for recommending and/or predicting drug groups suitable for development by individual pharmaceutical companies. It combines association rule learning, collaborative filtering, and content-based filtering approaches for enterprise-customized recommendations. In the case of content-based filtering applying a random forest classification algorithm, the accuracy and area under curve were 78% and 0.74, respectively. In particular, the DDR model was applied to predict the success probability of companies developing Coronavirus disease 2019 (COVID-19) vaccines. It was demonstrated that the higher the predicted score from the DDR model, the more progress in the clinical phase of the COVID-19 vaccine development. Although our approach has limitations that should be improved, it makes scientific as well as industrial contributions in that the DDR model can support rational decision-making prior to initiating drug development by considering not only technical aspects but also company-related variables.

Using combined clustering algorithms and association rules for better management of the amount of water delivered to the irrigation network of Abyek Plain, Iran

Using combined clustering algorithms and association rules for better management of the amount of water delivered to the irrigation network of Abyek Plain, Iran

A Biological Data-Driven Mining Technique by Using Hybrid Classifiers With Rough Set

Biological data classification and analysis are significant for living organs. A biological data classification is an approach that classifies the organs into a particular group based on their features and characteristics. The objective of this paper is to establish a hybrid approach with naive Bayes, apriori algorithm, and KNN classifier that generates optimal classification rules for finding biological pattern matching. The authors create combined association rules by using naïve Bayes and apriori approach with a rough set for next sequence prediction. First, the large DNA sequence is reduced by using k-nearest approach. They apply association rules by using naïve Bayes and apriori approach for the next sequence pattern. The hybrid approach provides more accuracy than single classifier for biological sequence prediction. The optimized hybrid process needs less execution time for rule generation for massive biological data analysis. The results established that the hybrid approach generally outperforms the other association rule generation approach.

Research on decision support system of sports assistant teaching and training based on association rules and support vector machine

In the decision-making system of sports assistant teaching and training, the performance of such system is not robust to different situations with a low accuracy. To solve the problems in the decision-making system, we proposed a decision-making method combining association rules and support vector machine (SVM) in this paper. First of all, we give a computer-aided decision support system for sports assistant learning and teaching training, which is fully elaborated from three aspects: virtual reality (VR) technology, VR based sports assistant learning and teaching and situational cognition, and VR based sports assistant learning and teaching mode. After that, the paper gives the feature extraction of sports auxiliary teaching training through association rules and the decision-making of the extracted association rules by SVM. We have done two different experiments for both association rules mining and SVM on both experiment group and control group of databases. Experimental results have shown that the training characteristics of sports auxiliary teaching very well. In the decision support of association rules, compared with the existing BP neural network, linear discriminant analysis and naive Bayes and other methods, the SVM method has better effect of action recognition in decision support system of sports assistant teaching and training. The robustness is the best for the application of SVM. We provide a new perspective for the decision support of sports auxiliary teaching training by using association rules and SVM. Through the method of this paper, we can obtain better decision-making effect and more robust process of sports auxiliary teaching and training.