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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

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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

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.

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

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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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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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Toward an intelligent tourism recommendation system based on artificial intelligence and IoT using apriori algorithm

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SOFT COMPUTING 2023年第24期27卷 19159-19177页

作者： Song, Yang He, Yingwei Sichuan Int Studies Univ Chengdu Inst Sch Int Business Chengdu 611844 Sichuan Peoples R China Beijing Guantao Zhongmao Wuhan Law Firm Wuhan 430062 Hubei Peoples R China

In recent years, the rapid development of the Internet has promoted the continuous expansion of the scale of China's tourism industry, and the amount of tourism data has surged. However, tourists need help bringing personal interest and high-value data from the plethora of tourism information. The rise of artificial intelligence has transformed traditional tourism into an intelligent, data-driven industry. This shift has generated vast tourism data, offering both opportunities and challenges. The paper discusses an AI and IoT-based Intelligent Tourism Recommendation System (ITRS) that offers travelers predefined itineraries, personalized suggestions, and tourism insights. This system simplifies attraction discovery, unveiling hidden value within tourism data at the intersection of AI and IoT. The present study thoroughly investigates AI-based recommendation algorithms before delving into the system's architecture. It categorizes user-based, project-based, and article-based collaborative filtering methodologies tailored to specific goals. First, thoroughly examine AI-based recommendation algorithms before delving into the system architecture. Second, categorize collaborative filtering methods as user-based, project-based, and article-based, each tailored to specific objectives. Third, delve into the apriori algorithm's complexity within the context of weighted association rules and introduce an enhanced iteration for improved efficiency. The proposed scheme encompasses an elaborate ITRS plan featuring a user interest model and a client module, crucial for the computation and analysis of users' long-term and short-term interests. Rigorous performance testing confirms the ITRS's superiority across varying support levels, with experimental results demonstrating the apriori algorithm's exceptional accuracy, achieving a 94.3% improvement over other methods. The apriori algorithm is better than traditional recommendation algorithms such as Linear Regression, Logistic Regre

关键词： Internet Artificial intelligence Intelligent tourism recommendation system apriori algorithm

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Average-case performance of the apriori algorithm

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SIAM JOURNAL ON COMPUTING 2004年第5期33卷 1223-1260页

作者： Purdom, PW Van Gucht, D Groth, DP Indiana Univ Dept Comp Sci Bloomington IN 47405 USA Indiana Univ Sch Informat Bloomington IN 47405 USA

The failure rate of the apriori algorithm is studied analytically for the case of random shoppers. The time needed by the apriori algorithm is determined by the number of item sets that are output (successes: item sets that occur in at least k baskets) and the number of item sets that are counted but not output (failures: item sets where all subsets of the item set occur in at least k baskets but the full set occurs in less than k baskets). The number of successes is a property of the data;no algorithm that is required to output each success can avoid doing work associated with the successes. The number of failures is a property of both the algorithm and the data. We find that under a wide range of conditions the performance of the apriori algorithm is almost as bad as is permitted under sophisticated worst-case analyses. In particular, there is usually a bad level with two properties: (1) it is the level where nearly all of the work is done, and (2) nearly all item sets counted are failures. Let l be the level with the most successes, and let the number of successes on level l be approximately ((m)(l)) for some m. Then, typically, the apriori algorithm has total output proportional to approximately ((m)(l)) and total work proportional to approximately ((m)(l+1)). In addition m is usually much larger than l, so the ratio of work to output is proportional to approximately m/(l+1). The analytical results for random shoppers are compared against measurements for three data sets. These data sets are more like the usual applications of the algorithm. In particular, the buying patterns of the various shoppers are highly correlated. For most thresholds, these data sets also have a bad level. Thus, under most conditions nearly all of the work done by the apriori algorithm consists in counting item sets that fail.

关键词： data mining algorithm analysis apriori algorithm

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A new wind speed forecasting strategy based on the chaotic time series modelling technique and the apriori algorithm

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ENERGY CONVERSION AND MANAGEMENT 2014年 84卷 140-151页

作者： Guo, Zhenhai Chi, Dezhong Wu, Jie Zhang, Wenyu Chinese Acad Sci Inst Atmospher Phys State Key Lab Numer Modeling Atmospher Sci & Geop Beijing 100029 Peoples R China Lanzhou Univ Sch Math & Stat Lanzhou 730000 Peoples R China Lanzhou Univ Coll Atmospher Sci Key Lab Arid Climat Change & Reducing Disaster Ga Key Lab Semiarid Climate ChangeMinist Educ Lanzhou 730000 Peoples R China

Wind energy has been the fastest growing renewable energy resource in recent years. Because of the intermittent nature of wind, wind power is a fluctuating source of electrical energy. Therefore, to minimize the impact of wind power on the electrical grid, accurate and reliable wind power forecasting is mandatory. In this paper, a new wind speed forecasting approach based on based on the chaotic time series modelling technique and the apriori algorithm has been developed. The new approach consists of four procedures: (I) Clustering by using the k-means clustering approach;(II) Employing the apriori algorithm to discover the association rules;(III) Forecasting the wind speed according to the chaotic time series forecasting model;and (IV) Correcting the forecasted wind speed data using the associated rules discovered previously. This procedure has been verified by 31-day-ahead daily average wind speed forecasting case studies, which employed the wind speed and other meteorological data collected from four meteorological stations located in the Hexi Corridor area of China. The results of these case studies reveal that the chaotic forecasting model can efficiently improve the accuracy of the wind speed forecasting, and the apriori algorithm can effectively discover the association rules between the wind speed and other meteorological factors. In addition, the correction results demonstrate that the association rules discovered by the apriori algorithm have powerful capacities in handling the forecasted wind speed values correction when the forecasted values do not match the classification discovered by the association rules. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Wind energy Forecasting Chaotic time series apriori algorithm Correction

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A Study on Basketball Techniques and Tactics Based on apriori algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2018年第2期102卷 1203-1212页

作者： Zhong, Xiwei Dongguan Polytech Dongguan Peoples R China

At present, the informatization of basketball matches is gradually increasing. Massive data accumulated in the basketball database can be effectively used through data mining. This study analyses basketball techniques and tactics based on the apriori algorithm. First, this study introduces apriori algorithm in data mining of association rules. Then, based on apriori algorithm, this study explores most commonly used technical actions in basketball games to provide reference for the training of players and coaches. Improved algorithms and data of basketball games were used to make association rules analysis to analyze technical and tactical characteristics. The algorithm generates association rules based on the frequent itemsets of basketball technical moves. The improved algorithm can dig out strongly connected technical actions in basketball games. The analysis has significantly guiding function.

关键词： apriori algorithm Basketball Action mining

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