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Exploring hierarchy shift of travelers' public transport dependence using an improved apriori algorithm

TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH 2025年第1期17卷 72-85页

作者： Hu, Song Wang, Lin Weng, Jiancheng Zhou, Wei Qian, Yue Wang, Haipeng Res Inst Highway Minist Transport Natl Intelligent Transport Syst Ctr Engn & Technol Beijing Peoples R China Beijing Univ Technol Beijing Key Lab Traff Engn 100 Pingleyuan Beijing 100124 Peoples R China Minist Transport Peoples Republ China Comm Experts Beijing Peoples R China Beijing Jiaotong Univ Beijing Peoples R China

Special external environments will lead to significant changes in the use behavior and dependence degree of different PT travellers, but it is difficult to analyze the mechanism of the hierarchy shift of travelers' public transportation (PT) dependence. Exploring travelers' dependence on PT is conducive to understanding individuals' travel choice behavior and optimizing PT operation organizations. Developing methods for analyzing the internal causal relationship between travelers' dependence on PT and the key influencing factors under the special condition is an issue. Therefore, the individual travel chains are constructed by associating and matching the multisource PT big data and travel survey data. Thereafter, the K-means algorithm and an improved apriori algorithm are developed to mine the frequent association rules of groups, and a framework of cross-hierarchy policy implications is derived based on the differences in association rules. Finally, the stated preference survey method is used to measure the effectiveness of the policies.

关键词： Public transport dependence K-means algorithm apriori algorithm association rules

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Towards Smarter Maritime Security: apriori algorithm Optimization in Serverless Environments 16

Towards Smarter Maritime Security: Apriori Algorithm Optimiz...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Shahin, Mahtab Rahimpour, Saeed Ghasempouri, Tara Bauk, Sanja Fahringer, Thomas Draheim, Dirk Estonian Maritime Academy Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia Distributed and Parallel Systems Group University of Innsbruck Tirol Austria Information Systems Group Tallinn University of Technology Tallinn Estonia

This paper presents an optimized implementation of the apriori algorithm tailored for large-scale data mining in cloud-native, serverless environments, utilizing real-world fuel datasets. Our approach achieves a 28% reduction in execution time and a 22% decrease in memory consumption compared to traditional distributed apriori methods. The study leverages high-dimensional fuel datasets, spanning from 2020 to 2050, to evaluate scalability and efficiency in processing energy-related data. By employing advanced synchronization and deferred partitioning strategies, communication overhead is significantly reduced, improving performance while effectively balancing computational loads across distributed nodes. Security measures, including AES-256 encryption and role-based access control (RBAC), are incorporated to safeguard data confidentiality and ensure compliance with regulatory frameworks. The proposed solution scales efficiently for datasets up to 1 million records, demonstrating applicability across domains such as transportation and logistics. Future work will explore adaptive partitioning techniques, hybrid cloud architectures, and AI-driven predictive analytics to further enhance scalability and operational efficiency in serverless multi-cloud systems. © 2025 Elsevier B.V.. All rights reserved.

关键词： apriori algorithm Big Data Cloud-Native Architectures Data Mining Distributed Computing Maritime Serverless Computing

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Research on User Profiling based on apriori algorithm for Power Marketing Big Data 5

Research on User Profiling based on Apriori Algorithm for Po...

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5th International Conference on Telecommunications, Optics, and Computer Science, TOCS 2024

作者： Xu, Yuanbin Wang, Dadi Liu, Xinyi Pan, Yongchao State Grid Info & Telecom Group Co. Ltd. Beijing 100052 China Beijing Branch of China Power Industry Group State Grid Info & Telecom Group Co. Ltd. Beijing 100052 China Information Industry Research Institute State Grid Info & Telecom Group Co. Ltd. Beijing 100052 China Marketing Department State Grid Liaoning Electric Power Co. Ltd. Shenyang 110000 China

ISBN: (纸本)9781510691667

The types of electricity users are increasing, electricity consumption is growing significantly, and user demands for power services are becoming more diverse. By leveraging big data technology to deepen the analysis of customer behavior characteristics, a precise customer profiling strategy has been developed, resulting in a user-centric profiling system. This system labels user data to abstract key features such as individual attributes, consumption preferences, and behavioral habits, providing a comprehensive depiction of user information. The study first utilizes a Black Friday shopping platform dataset to extract static and dynamic data, which are then used to generate three types of labels: statistical labels, rule-based labels, and mining-based labels. These labels correspond to the individual attribute module, individual consumption capability module, and product preference module, respectively, offering precise user services and personalized recommendations in the field of electricity marketing. © 2025 SPIE.

关键词： apriori algorithm association rules electricity marketing personalized labels user profiling

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Towards Smarter Maritime Security: apriori algorithm Optimization in Serverless Environments

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Procedia Computer Science 2025年 257卷 452-459页

作者： Mahtab Shahin Saeed Rahimpour Tara Ghasempouri Sanja Bauk Thomas Fahringer Dirk Draheim Estonian Maritime Academy Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia Distributed and Parallel Systems Group University of Innsbruck Tirol Austria Information Systems Group Tallinn University of Technology Tallinn Estonia

关键词： Cloud-Native Architectures Serverless Computing Data Mining apriori algorithm Distributed Computing Big Data Maritime

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Optimization of Hemorrhagic Stroke Strategy Based on Random Forest and apriori algorithm 24

Optimization of Hemorrhagic Stroke Strategy Based on Random ...

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Proceedings of the 2024 4th International Conference on Computational Modeling, Simulation and Data Analysis

作者： Tao Lin Yu Wan Dejin Ma Yiheng Zhang Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9798400711831

After a hemorrhagic stroke, patients may experience increased hematoma expansion and changes in edema volume. Timely and effective prognosis diagnosis and evaluation can improve the quality of life and survival rate of patients. This article is based on a data-driven approach, integrating imaging features and clinical patient information, and using random forest and apriori algorithms for analysis, diagnosis, prediction, and evaluation. This provides strong support for clinical decision-making. The research results indicate that the model can effectively predict the probability of hematoma enlargement in patients and evaluate the influence of key factors such as edema volume.

关键词： apriori algorithm

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An Efficient Approach to Association Rule Mining Using the apriori algorithm: A Case Study on Enhancing Sports Performance in University Students 24

An Efficient Approach to Association Rule Mining Using the A...

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Proceedings of the 2024 International Conference on Sports Technology and Performance Analysis

作者： Wanting Lv Shan Li School of Physical Education Xi'an Physical Education University Xi'an China

ISBN: (纸本)9798400712234

The 2022 edition of the Dietary Guidelines for Chinese Locals indicates that the occurrence of overweight and excessive weight among Chinese university students gets on the rise, while their sporting activities efficiency is decreasing. This write-up offers research study focused on finding possible remedies to boost university students' sports performance. An association policy mining method based on the apriori algorithm was made use of to discover the relationship between different workout programs and the failing indicators observed in fitness tests. The results revealed that HIIT performed at 90%-97% of HRmax worked in reducing body fat percentage amongst pupils with an overweight BMI and was specifically beneficial for boosting explosive rate and muscular cardio endurance. On the other hand, HIIT conducted at 80%-89% HRmax was a lot more efficient in improving cardiorespiratory feature and muscular stamina. Furthermore, MICT at 75%-79% HRmax can effectively decrease body fat percentage and enhance adaptability in students with overweight BMI. This paper highlights the strong link between computer technology and the advancement of tailored exercise plans, demonstrating just how computer system formulas can extra precisely enhance the sports performance of university students.

关键词： apriori algorithm

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Research on the Construction and Decision Optimization of Dynamic Evaluation System for Accounting Education Quality Based on apriori algorithm 25

Research on the Construction and Decision Optimization of Dy...

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Proceedings of the 2025 International Conference on Big Data and Informatization Education

作者： Yang Wang School of Economics and Management Dalian University of Science and Technology Dalian Liaoning China

ISBN: (纸本)9798400714405

The deep integration of artificial intelligence and big data technology puts forward higher requirements for the dynamic adaptability of education management system. Aiming at the problems such as weak curriculum relevance and lagging management decisions in the quality assessment of accounting education, this study constructs a dynamic assessment system for the quality of accounting education based on the apriori algorithm and multi-source data-driven. By integrating 2.1TB of daily structured and unstructured data (including accounting practice operation logs, course grades, and case teaching videos), the system realizes three core functions: course group association rule mining (support level 0.09, confidence level 0.62), learner 32-dimensional ability portrait modeling and resource accurate recommendation (match level 82%). The empirical results show that the system can improve the efficiency of management decision making by 58%, and the satisfaction of students' personalized service is 91%, and the time series analysis validates the significant promoting effect of data structure courses on professional competence cultivation (Pearson coefficient 0.62, p<0.01). The research breaks through the static assessment framework and provides a closed-loop solution of “monitoring - early warning - optimization” for Accounting Education management, but it needs to further explore multi-modal data fusion and regional collaboration mechanisms.

关键词： apriori algorithm

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apriori algorithm-Based Learning Behavior Mining for Mobile Education Platforms

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MOBILE NETWORKS & APPLICATIONS 2024年 1-13页

作者： Hong, Mei Alwadain, Ayed Alzahrani, Ahmed Ibrahim Changchun Univ Changchun 130000 Peoples R China King Saud Univ Community Coll Comp Sci Dept Riyadh 11437 Saudi Arabia

In order to promote learners' learning effectiveness and improve the accuracy of learning behavior mining, this paper conducts research on the apriori algorithm based learning behavior mining on mobile education platforms. Firstly, web crawler technology is used to capture the behavioral information of learners during the learning process on the mobile education platform to construct learner profiles, and preprocess the sub-network set data of learning behaviors. Secondly, a Hash table is constructed to improve the apriori algorithm to extract the learning behavior characteristics of learners on the mobile education platform. Then, a Stacking ensemble learning model is built to determine four base learners for model training. Finally, the Stacking ensemble learning model is improved with a chain rule, and conducting k-fold cross-validation to achieve data mining of learning behaviors on the mobile education platform. Comparative experiments have proven that when using the method proposed in this paper for data mining of learning behaviors on the mobile education platform, the normalized difference precision is always above 90%, the mAP value is always above 93%, the mining scope coverage rate is maintained above 90%, and the comprehensiveness is kept above 90%. This indicates that applying the method proposed in this paper to the data mining of learning behaviors on the mobile education platform can improve the accuracy of data mining and has a good mining effect.

关键词： apriori algorithm Mobile education platform Learning behavior Data mining Hash table Stacking ensemble learning

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Digital Twin-based Quality Management Method for the Assembly Process of Aerospace Products with the Grey-Markov Model and apriori algorithm

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Chinese Journal of Mechanical Engineering 2022年第5期35卷 66-86页

作者： Cunbo Zhuang Ziwen Liu Jianhua Liu Hailong Ma Sikuan Zhai Ying Wu Laboratory of Digital Manufacturing School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing 314000China Shanghai Institute of Spacecraft Equipment Shanghai 200240China

The assembly process of aerospace products such as satellites and rockets has the characteristics of single-or small-batch production,a long development period,high reliability,and frequent *** to predict and avoid quality abnormalities,quickly locate their causes,and improve product assembly quality and efficiency are urgent engineering *** the core technology to realize the integration of virtual and physical space,digital twin(DT)technology can make full use of the low cost,high efficiency,and predictable advantages of digital space to provide a feasible solution to such ***,a quality management method for the assembly process of aerospace products based on DT is *** that traditional quality control methods for the assembly process of aerospace products are mostly post-inspection,the Grey-Markov model and T-K control chart are used with a small sample of assembly quality data to predict the value of quality data and the status of an assembly *** apriori algorithm is applied to mine the strong association rules related to quality data anomalies and uncontrolled assembly systems so as to solve the issue that the causes of abnormal quality are complicated and difficult to *** implementation of the proposed approach is described,taking the collected centroid data of an aerospace product’s cabin,one of the key quality data in the assembly process of aerospace products,as an example.A DT-based quality management system for the assembly process of aerospace products is developed,which can effectively improve the efficiency of quality management for the assembly process of aerospace products and reduce quality abnormalities.

关键词： Quality management Digital twin Assembly process Aerospace product Grey Markov model apriori algorithm

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apriori algorithm on Web Logs for Mining Frequent Link

Apriori Algorithm on Web Logs for Mining Frequent Link

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IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)

作者： Sathya, M. Devi, P. Isakki Ayya Nadar Janaki Ammal Coll Dept Comp Sci Sivakasi Tamil Nadu India Ayya Nadar Janaki Ammal Coll Dept Comp Applicat Sivakasi Tamil Nadu India

ISBN: (纸本)9781509047789

Web mining is a combination of data mining and World Wide Web. It consists of three types namely web structure mining, web content mining and web usage mining. Web Usage Mining is one of the parts of web mining and extracts the web users' behavior from web log file. This paper consists of three phases. The first one is data preprocessing phase, which is the most important one because it makes the data with good quality. This can be done by data cleaning, user identification, and session identification. The next one is pattern discovery phase;in this the users' navigational pattern and rules are extracted by using apriori algorithm. Final one is pattern analysis phase, which is used to analyze and visualize the rules. The aim of this paper is to identify the frequent link from web log data by using the apriori algorithm.

关键词： Web mining Web usage mining Data cleaning User Identification Session Identification apriori algorithm

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