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Research on Analysis of Students Using Mobile Phones in Ideological and Political Classrooms by apriori algorithm of Association Rules

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Procedia Computer Science 2022年 208卷 12-17页

作者： Pan Gao

The phenomenon of college students using mobile phones in class is very common. Few students can do not use mobile phones in class, and most students have great dependence on mobile phones. Although students will use mobile phones in class according to their needs, in most cases the number of students using mobile phones will increase with the increase of classroom teaching time. In this paper, the association rule mining algorithm apriori algorithm is used to analyze the current situation of mobile phone use of full-time college students in ideological and political courses in Suzhou Vocational University. Then, the apriori algorithm based on association rules analyzes the mobile phone usage and improvement of students in ideological and political courses: cluster analysis is introduced in the preprocessing stage, and finally the algorithm is applied to the learning guidance of students' ideological and political courses, and points out the use of mobile phones by students in class. The root cause and further in-depth research on the use of mobile phone discussions in ideological and political classes.

关键词： Association rules apriori algorithm using mobile phones ideological political courses

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Data Dependence Analysis for Defects Data of Relay Protection Devices Based on apriori algorithm

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IEEE ACCESS 2020年 8卷 120647-120653页

作者： Tian, Mingwei Zhang, Lie Guo, Peng Zhang, Hanfang Chen, Qian Li, Yanfei Xue, Ancheng North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China China Elect Power Res Inst Beijing 100192 Peoples R China

Currently, a large amount of defect data in relay protection devices (RPDs) is accumulated in operation. However, the defect data dependence analysis is absence and thus it could not meet the demand for further improving the management and operation RPDs. Based on 7-years defect data of RPDs in SGCC, this paper discovers the association rules (ARs) of defect data based on the apriori algorithm. In detail, the ARs among different categories of PRDs, such as defect parts and defect causes are discovered and analyzed. Furthermore, the family characteristics of defects are illustrated, with the defect data of RPDs from different manufacturers. The analysis results show that the apriori method can effectively reveal the hidden information in the defect data, such as the ARs between the vulnerable parts of RPDs, defect causes and other factors.

关键词： Relay protection devices (RPDs) defect analysis data mining association rules (ARs) apriori algorithm

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An improved apriori algorithm based on support weight matrix for data mining in transaction database

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年第2期11卷 495-501页

作者： Sun, Li-na Henan Univ Minsheng Coll Kaifeng 475001 Henan Peoples R China

Data mining is a process to discover hidden information or knowledge automatically from huge database. In order to reduce the number of scanning databases and reflect the importance of different items and transaction so as to extract more valuable information, an improved apriori algorithm is proposed in this paper, which is to build the 0-1 transaction matrix by scanning transaction database for getting the weighted support and confidence. The items and transactions is weighted to reflect the importance in the transaction database. The experiment results, both qualitative and quantitative, have shown that our improved algorithm shortens the running time and reduces the memory requirement and the number of I/O operations. Meanwhile, the support for rare items tends to increase, while the support for other items decreases slightly, thus the hidden and valuable items can be effectively extracted.

关键词： Data mining apriori algorithm Weight matrix Support and confidence k-Itemset

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Knowledge Discovery from Library Automation via Bibliomining using the apriori algorithm

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GAZI UNIVERSITY JOURNAL OF SCIENCE 2022年第4期35卷 1344-1357页

作者： Firat, Ishak Aslay, Fulya Erzincan Binali Yildirim Univ Elect Elect Engn Dept TR-24060 Erzincan Turkey Erzincan Binali Yildirim Univ Comp Engn Dept TR-24060 Erzincan Turkey

Today which is called as the digital age with the considerably developing information systems, the constant increase in the data amount being recorded has revealed the concept of big data. Obtaining the strategic information which is crucial for decision-makers especially in managerial terms is only possible through processing these big data with accurate techniques. Data mining techniques have frequently been used in recent years in order to reach meaningful and useful knowledge among data stacks. In this study, the apriori algorithm was used for the managers of university library information systems, which provide data-oriented service, to make investment decisions in the future effectively and create user profiles. Within the scope of the study, an application was performed on the basis of an information system comprising of real data of Erzincan Binali Yildirim University Central Library. By means of association rules, which are one of the descriptive models of data mining, ten different association rules regarding the joint borrowing of publications were applied and results were obtained in the confidence intervals of 57.1% and 95.8%. In addition, information such as library inventory, member profile, and publication borrowing habits were obtained and evaluations were made in line with this information at the end of the study.

关键词： Association analysis apriori algorithm Bibliomining Library information system Data mining

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A Methodological Approach for Mining the User Requirements Using apriori algorithm

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JOURNAL OF CASES ON INFORMATION TECHNOLOGY 2020年第4期22卷 1-30页

作者： Soni, Anuja Saxena, Anand Bajaj, Parul Univ Delhi Deen Dayal Upadhyaya Coll New Delhi India DAV Publ Sch Gurugram India

Users of enterprise software are multiple, and their requirements are diverse. Often their specifications are masked by mundane details and at times are vague too. Acknowledging these complexities in requirements engineering, the paper proposes a multistage methodological approach based on apriori algorithm, a data mining technique. It extracts useful information from the given data on the criteria of mutual association and sufficient frequency. The user requirements captured through interviews and brainstorming are pre-processed for eliminating unnecessary stop words and developing a uniform structure of small stories. Mutual association and occurrence of the requirements are represented through association rules and rule metrics, for example, 'Lift', 'Support', and 'Confidence'. The requirements having strong and moderate association are placed in 'Top Priority List';those with nominal, weak, or nil association are placed in 'Low Priority List'. Gap analysis is employed to validate the defined requirements with respect to stakeholders' expectations. The complete and correct lists of requirements significantly influence the client satisfaction, software development process, and its eventual success.

关键词： apriori algorithm Association Rules Confidence Gap Analysis Lift Requirements Engineering Support User Requirements

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Implementation of Web Log Mining Device Under apriori algorithm Improvement and Confidence Formula Optimization

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY AND WEB ENGINEERING 2020年第4期15卷 53-71页

作者： Zhu, Lihua Changzhou Coll Informat Technol Changzhou Peoples R China

With the increasing number of internet users, a large number of network alarm information increases, resulting in the increasing pressure of SMS gateway and frequent alarm delay. Therefore, in order to effectively improve the above problems, the article is based on the improved apriori algorithm and confidence formula, points and grabbing module, model training module and the test evaluation module device, three steps to realize the web log mining and mining system design for the data acquisition module, data preprocessing module, mining model building blocks, mining model checking module and mining model analysis and evaluation module five modules. Finally, the Python program was used to verify the test data of about two million pieces of original alarm data in a company's network management database for a consecutive month. The verification results show that the design of this paper has greatly reduced the number of original alarms and completed the merging of related rules.

关键词： apriori algorithm Confidence Web Log Mining

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Application of improved time series apriori algorithm by frequent itemsets in association rule data mining based on temporal constraint

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EVOLUTIONARY INTELLIGENCE 2020年第1期13卷 39-49页

作者： Wang, Chunxia Zheng, Xiaoyue Shangqiu Normal Univ Sch Informat Technol Shangqiu 476000 Henan Peoples R China

The basic idea of apriori algorithm is first introduced in this paper, which is to find all frequent sets in a transaction. The frequent requirements of these frequent sets are greater than or equal to the minimum support of the set. On this basis, the working principle of the traditional apriori algorithm is analyzed, and the existing problems are pointed out. To solve these problems, an improved apriori algorithm is proposed for time series of frequent itemsets. Finally, on the basis of analyzing the methods and processes of mining association rules for time series, this improved time series apriori algorithm for frequent itemsets is applied to mining association rules based on time constraints. The experimental results show that the improved apriori algorithm is better than the traditional one in storage space.

关键词： Time series apriori algorithm Frequent item Data mining Association rule Temporal constraint

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Big Data Correlation Analysis Model of Distribution Network Operation Based on apriori Correlation algorithm 24

Big Data Correlation Analysis Model of Distribution Network ...

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2024 International Conference on Big Data and Digital Management, ICBDDM 2024

作者： Wang, Ying Beijing Zhongdian Feihua Communication Co. LTD China

ISBN: (纸本)9798400710278

With the deepening of enterprise digitalization, data mining technology has become a key tool to extract valuable information from massive data. Among them, association rule extraction, as the core content of data mining, is of great significance for revealing the complex relationship between data. As a common method for association rule mining, the apriori algorithm provides powerful data analysis and decision support for enterprises. In this paper, the big data correlation analysis model of distribution network operation based on apriori correlation algorithm and the optimized apriori algorithm are used to mine the strong correlation law between various influencing factors in the distribution network, and establish a fault environment pattern identification database of distribution network, so as to lay the foundation for the early warning of distribution network operation risk and further improve the stability of power supply. © 2024 Copyright held by the owner/author(s).

关键词： apriori algorithm Data mining Enterprise digitalization

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An Extension of the apriori algorithm for Finding Frequent Items 6

An Extension of the Apriori Algorithm for Finding Frequent I...

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6th International Conference on Web Research (ICWR)

作者： Karimtabar, Noorollah Fard, Mohammad Javad Shayegan Tech & Vocat Univ Dept Comp Engn Babol Iran Univ Sci & Culture Dept Comp Engn Tehran Iran

ISBN: (纸本)9781728110516

The main purpose of data mining is to discover hidden and valuable knowledge from data. The apriori algorithm is inefficient due to bulky deals of searching in a dataset. Bearing this in mind, this paper proposes an improved algorithm from apriori using an intelligent method. Proposing an intelligent method in this study is to fulfill two purposes: First, we demonstrated that to create itemsets, instead of adding one item at each step, several items could be added. With this operation, the number of k-itemset steps will decline. Secondly, we have proved that by storing the transaction number of each itemset, there would be a diminishment in the time required for the dataset searches to find the frequent k-itemset in each step. To evaluate the performance, the Intelligent apriori (IAP) algorithm has been compared with the MDC algorithm. The results of this experiment exhibit that since the transaction scans used to obtain the itemset momentously reduced in number, there was a considerable fall in the runtime needed to obtain a frequent itemset by the proposed algorithm. In this study, the time required to generate frequent items had a 46% reduction compared to that of the MDC_apriori algorithm.

关键词： Data Mining apriori algorithm Intelligent Method Frequent Itemset

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Research on parallelization of apriori algorithm in association rule mining

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Procedia Computer Science 2021年 183卷 641-647页

作者： Huan-Bin Wang Yang-Jun Gao College of Equipment Management And Unmanned Aerial Vehicle Engineering of Airforce Engineering University ShanXi Xi’an 710051 China

Aiming at the performance bottleneck of traditional apriori algorithm when the data set is slightly large, this paper adopts the idea of parallelization and improves the apriori algorithm based on MapReduce model. Firstly, the local frequent itemsets on each sub node in the cluster are calculated, then all the local frequent itemsets are merged into the global candidate itemsets, and finally, the frequent itemsets that meet the conditions are filtered according to the minimum support threshold. The advantage of the improved algorithm is that it only needs to scan the transaction database twice and calculate the frequent item set in parallel, which improves the efficiency of the algorithm.

关键词： Association rules apriori algorithm MapReduce Parallelization

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