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Dynamic early warning system of College Students' target course performance based on improved apriori algorithm

JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2021年第6期21卷 1779-1795页

作者： Sun, Guohong Guo, Shaocui Hao, Guo Yang, Wenbo Yantai Vocat Coll Sch Open Educ Yantai 264670 Shandong Peoples R China Yantai Vocat Coll Dept Architectural Engn Yantai 264670 Shangdong Peoples R China Yantai Vocat Coll Gen Affairs Off Yantai Shangdong Peoples R China

The early warning system of College Students' target course achievement is an important part of the educational administration system in Colleges and universities. This paper proposes to use some techniques of association principle to mine a large amount of data in the performance system to a certain extent, and obtain available rules from the data. Based on the characteristics and shortcomings of apriori algorithm, an improved apriori is proposed. The algorithm can process and mine the data in the early warning system of College Students' scores, and finally obtain the management principles, thus forming an effective early warning for the course learning. In order to promote the improvement of students' academic performance and achieve the ultimate goal of cultivating excellent talents in Colleges and universities.

关键词： improved apriori algorithm college students target course achievement early warning system

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Combining an improved apriori algorithm and Social Network analysis to identify the unique sequential features of individual household electricity consumption behaviours

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ADVANCES IN BUILDING ENERGY RESEARCH 2024年第4期18卷 353-383页

作者： Li, Li Deng, Shiyu Xiong, Yichen Wang, Jianjun Cai, Hua Zhang, Jian Guo, Songliang Zhang, Jing Liu, Fang Li, Tianfeng Beijing Informat Sci & Technol Univ Sch Econ & Management Beijing Peoples R China Beijing Informat Sci & Technol Univ Sch Comp Sci Beijing Peoples R China Beijing Informat Sci & Technol Univ Lab Big Data Decis Making Green Dev Beijing Peoples R China Beijing Informat Sci & Technol Univ Beijing Int Sci & Technol Cooperat Base Intelligen Beijing Peoples R China North China Elect Power Univ Sch Econ & Management Beijing Peoples R China Purdue Univ Sch Ind Engn W Lafayette IN USA Purdue Univ Environm & Ecol Engn W Lafayette IN USA Beijing Univ Posts & Telecommun Sch Econ & Management Beijing Peoples R China Wuhan Univ China Inst Boundary & Ocean Studies Wuhan Peoples R China Beijing Informat Sci & Technol Univ Jianxiangqiao Campus35 Beisihuan Middle Rd Beijing Peoples R China

The household's electricity consumption behaviour follows a unique sequential pattern and can be identified by analyzing the electricity consumption data with fine granularity in a smart home context. This particular sequence is concretely embodied in a series of electrical activities with temporal association relationships, thus forming a unique household electrical appliance activity-based chain (EAAC). In this study, we propose a method that combines an improved apriori algorithm and Social Network to construct a household EAACs. First, we improved the apriori algorithm (apriori (1,1)) by combining the temporal and transactional sequences and used it to mine the time-series activity associations among different household appliances. After that, we used the Sliding Window concept to splice the time-series associations and obtain the unique EAACs for each household. Next, we visualized the EAACs using the Social Network analysis to reflect the unique sequential features of household electricity consumption behaviours. Finally, the model was validated by analyzing two families' actual household electricity consumption data, and future research directions were *** The apriori algorithm was improved by integrating a time factor to mine the temporal association relationships among different household electrical appliancesChaining the activities of household electrical appliances based on the sliding window principleExploring the unique sequential features of household electricity consumption by visualizing the EAACs through social network graph topology

关键词： Appliance activity chain improved apriori algorithm key nodes Sliding Window Social Network

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Frequent Itemset Mining using improved apriori algorithm with MapReduce

Frequent Itemset Mining using Improved Apriori Algorithm wit...

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3rd International Conference on Computing, Communication, Control and Automation (ICCUBEA)

作者： Tribhuvan, Seema A. Gavai, Nitin R. Vasgi, Bharti P. Sinhgad Coll Engn Dept Informat Technol Pune Maharashtra India

ISBN: (纸本)9781538640081

Data mining tools estimate future trends and behaviors, allowing businesses to make practical, knowledge-driven decisions. Association Rule mining is a very important data mining practice in different fields. In most of the data mining applications, finding frequent itemsets is the crucial issue needed to be addressed. Several algorithms like apriori, FP-Growth, and FUIT offered better solutions for mining frequent itemsets. Still, the features like automatic parallelization, fine load balancing, and distribution of data on large clusters, are needed to be achieved. improved apriori algorithm with MapReduce framework is used to solve these issues. Thus, it is possible to achieve parallelism and lessen the execution time. Here, number of mapreduce jobs are used to discover frequent itemsets of big datasets with the help of multiple computing nodes by applying parallelism among them. In this paper, the proposed system works on multiple nodes efficiently and execution time also reduced.

关键词： Association rule mining frequent item sets MapRecue improved apriori algorithm hash tables

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English Classroom Teaching Based on improved apriori algorithm 2

English Classroom Teaching Based on Improved Apriori Algorit...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Zheng, Shu Hu, Zhongbin Chengdu College of Arts and Sciences Chengdu China

ISBN: (纸本)9798350317558

Classroom teaching is the primary organizational formation of school teaching and the basic way of obtaining student's creativity and quality-oriented education. English teachers work from various backgrounds which include primary and secondary schools, universities, and colleges accommodating their teaching techniques to suit the different learning requirements of their students. However, only by maximizing their capability in information age could teachers understand the students better and fulfill the requirements of student learning in the environment of a network. In this research, the improved apriori algorithm (IAA) is proposed for English classroom teaching quality. The IAA is improved by utilizing a Boolean matrix for row-column compression which minimizes the transaction database scanning time and the Trie tree is employed to increase the search process. Initially, the questionnaire sample data is established to evaluate the teacher criteria for learning tasks. The data preprocessing is performed by employing data filtering and then the evaluation of filtered text is split into short sentences. Then, the Word2 vector approach is employed to extract the features. Finally, the IAA technique is performed for quality of English classroom teaching. When compared with the apriori algorithm (AA), the IAA achieves better student innovation ability and student satisfaction of 90% and 95% respectively. © 2024 IEEE.

关键词： Data filtering English classroom teaching quality improved apriori algorithm Student Word2 vector

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Application of improved apriori algorithm in Computer database Management System 2

Application of Improved Apriori Algorithm in Computer databa...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Dang, Haiyan Shao, Yuhan Dang, Wenyu Mingde Institute of Technology Shaanxi Xi'an China Dalian University of Foreign Languages Liaoning China Tianjin University of Finance and Economics Pearl River College Tianjin China

ISBN: (纸本)9798350317558

In order to improve the efficiency of association rule mining algorithm, an improved apriori algorithm is proposed in the computer database management system. On the basis of theoretical knowledge, the classical apriori algorithm for mining association rules is deeply analyzed. According to the characteristics of mining association rules in relational databases, a new coding algorithm - apriori coding is proposed, and the implementation steps are described in detail from two aspects of theory and process. The experimental results show that under different minimum critical value, the first 30000 records in the database are used for association rule mining. Minimum critical value for Sup are set to 5%, 10%, 15%, 20% and 25% respectively. When the minimum support critical value is small, the execution time of the encoded apriori algorithm is far less than that of the traditional apriori algorithm. Select the first 5000, 10000, 15000, 20000, 25000 and 30000 records from the database, and minimum critical value=20%.The apriori coding algorithm is more efficient than the traditional apriori algorithm, especially when the number of records in the database is large. The coding of apriori algorithm is compared with that of apriori algorithm, which verifies the effectiveness and superiority of the improved algorithm. © 2024 IEEE.

关键词： association rules Data mining database management improved apriori algorithm procurement data

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Application of an improved apriori algorithm in a mobile e-commerce recommendation system

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INDUSTRIAL MANAGEMENT & DATA SYSTEMS 2017年第2期117卷 287-303页

作者： Guo, Yan Wang, Minxi Li, Xin Chengdu Univ Technol Coll Management Sci Chengdu Peoples R China

Purpose - The purpose of this paper is to make the mobile e-commerce shopping more convenient and avoid information overload by a mobile e-commerce recommendation system using an improved apriori algorithm. Design/methodology/approach - Combined with the characteristics of the mobile e-commerce, an improved apriori algorithm was proposed and applied to the recommendation system. This paper makes products that are recommended to consumers valuable by improving the data mining efficiency. Finally, a Taobao online dress shop is used as an example to prove the effectiveness of an improved apriori algorithm in the mobile e-commerce recommendation system. Findings - The results of the experimental study clearly show that the mobile e-commerce recommendation system based on an improved apriori algorithm increases the efficiency of data mining to achieve the unity of real time and recommendation accuracy. Originality/value - The improved apriori algorithm is applied in the mobile e-commerce recommendation system solving the limitation of the visual interface in a mobile terminal and the mass data that are continuously generated. The proposed recommendation system provides greater prediction accuracy than conventional systems in data mining.

关键词： Data mining improved apriori algorithm Mobile e-commerce Recommendation system

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A Novel and improved apriori algorithm

A Novel and Improved Apriori Algorithm

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2014 International Conference on Vehicle,Mechanical and Electrical Engineering(ICVMEE 2014)

作者： Dongjuan Gu Lei Xia School of Information Nanchang Institute of science & Technology Modern Educational Technology Center Nanchang Institute of science & Technology

apriori algorithm is the classical algorithm in data mining association *** the apriori algorithm needs scan database for many times,it runs too *** order to improve the running efficiency,this paper improves the apriori algorithm based on the apriori *** improved idea is that it transforms the transaction database into corresponding 0-1 *** each vector and subsequent vector does inner product operation to receive *** comparing with the given minsupport,the rows and columns will be deleted if vector are less than the minsupport,so as to reduce the size of the rating matrix,improve the running *** the improved algorithm only needs to scan the database once when running,therefore the running speeding is more *** experiment also shows that this improved algorithm is efficient and feasible.

关键词： apriori Association Rules improved apriori algorithm Data Mining

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Data Mining and Risk Prediction Based on apriori improved algorithm for Lung Cancer

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JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2021年第7期93卷 795-809页

作者： Guo, Hong Liu, Hong Chen, JiaYou Zeng, Yan Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan Peoples R China Wuhan Third Hosp Intens Care Unit Wuhan Peoples R China

Starting from medical big data, this article uses data mining technology to analyze and study the pathogenic factors of lung cancer based on the lung cancer electronic medical record data from the oncology department of the authoritative third grade A hospital for many years. With respect to the processing of huge data from electronic medical records for lung cancer, traditional serial apriori algorithm has the disadvantages of scanning database frequently, running slowly and consuming large amount of memory resources. Therefore, an improved apriori algorithm based on MapReduce distributed computing model of Hadoop platform is proposed. The experimental cluster and lung cancer data mining experiments show that the improved apriori algorithm has higher execution efficiency and good system scalability in dealing with lung cancer big data, and can well mine the relationship between lung cancer and pathogenic factors, which has important guiding significance for assisting the clinical diagnosis and risk prediction of lung cancer.

关键词： Medical big data Hadoop platform improved apriori algorithm Lung cancer data mining Analysis of pathogenic factors Risk prediction

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An improved apriori-Based Personal Recommendation algorithm for E-commerce

An Improved Apriori-Based Personal Recommendation Algorithm ...

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3rd International Conference on Pervasive Computing and Applications

作者： Hu, Zhongyi Shen, Liangzhong Chen, Shengkai Wenzhou Univ Coll Comp Sci & Engn Wenzhou Zhejiang Peoples R China

ISBN: (纸本)9781424420209

In order to improve the quality of the recommended result, the Personalized Recommendation System should identify the similarity degree of visitor's accessing behavior so as to predict customers interests. The key technology is to calculate the similar distance among different objects over either all or only a subset of the dimensions. This paper, first of all, analyses the commonly-used methods and points out their shortages, and then proposes an improved apriori-Based Personal Recommendation algorithm for E-commerce. This algorithm considers overall the minable data source, users' Similarity Metric and K-Support Bound to get the data of those access web pages, construct a matrix model having relatively high purchasing power about customer behavior, get the similar access behavior over the all or partial property space with high efficiency, help the customer find out the merchandise he wishes to buy through the mine of the similar pattern character between latent buyer and high buyer, promote customer satisfaction and truly promote the sale achievements for the enterprise.

关键词： Personal Recommendation algorithm improved apriori algorithm Mining algorithm User Similarity E-commerce

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Japanese Teaching Quality Satisfaction Analysis With improved apriori algorithms Under Cloud Computing Platform

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COMPUTER SYSTEMS SCIENCE AND ENGINEERING 2020年第3期35卷 183-189页

作者： Cai, Lini Hunan City Univ Yiyang 413000 Hunan Peoples R China

In this paper, we use modern education concept and satisfaction theory to study the construction of a system used to evaluate Japanese teaching quality based on a satisfaction model. We use a cloud computing platform to mine the rules of Japanese teaching quality satisfaction by using an improved apriori algorithm to explore the impact of measurement indicators of teaching objectives, processes and results on overall satisfaction with Japanese teaching practices, so as to improve Japanese teaching in the future. Scientific decision-making, improvement of teaching practices, transformation and innovation of students' learning methods provide data reference and theoretical support.

关键词： Relevance Analysis improved apriori algorithm Japanese Teaching Quality Evaluation Satisfaction Model

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