Optimization of Teaching Management System Based on Association Rules Algorithm

Qing, Niu

doi:10.1155/2021/6688463

Cited by 6 publications

(5 citation statements)

References 30 publications

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“…Literature [12] examines how association rule algorithms can enhance the efficiency and effectiveness of teaching management systems within educational institutions. By incorporating data mining techniques into the development of the teaching management system, the laws and problems in the teaching process can be better mined, which in turn can provide more accurate and personalized teaching services and bring more convenience and value to the educational teaching work.…”

Section: Literature Reviewmentioning

confidence: 99%

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Digitalization of university education management applicable to association rule mining algorithms

Chen

2023

Applied Mathematics and Nonlinear Sciences

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In this paper, firstly, based on the digital construction of education management in colleges and universities, it is proposed to construct an education management system for colleges and universities to realize the digital development of education management and to design the function and database of the education management system of colleges and universities. Secondly, the Apriori algorithm in association rule mining algorithm is used to define the minimum support and minimum confidence in the variable parameter focus, then the support threshold and confidence threshold are set in the process of optimization connection and pruning, and for the shortcomings of traditional algorithms, the relevance metrics are cited here to improve the systematic data mining efficiency of the Apriori algorithm. Then, the simulation experiment environment and data set are selected, the evaluation index system is determined according to the system requirements, and the education management system based on the association rule mining algorithm is simulated and analyzed. The results show that when min_supp=70%, the Apriori algorithm generates a total of 60 frequent itemsets, and the running time is about 21.2s. Overall, the Apriori algorithm only reads the data set twice, so the program running time is shorter than the Eclat algorithm running time.

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Section: Literature Reviewmentioning

confidence: 99%

“…To address the shortcomings of the traditional algorithm, the relevance metric is invoked here, as shown in Equation (12). but this interest measure also has its limitations, that is, the interval of the value of…”

Section: Relevance Metricsmentioning

confidence: 99%

Digitalization of university education management applicable to association rule mining algorithms

Chen

2023

Applied Mathematics and Nonlinear Sciences

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“…It is worth noting that cur_Minsup is a temporal condition in D i+1 used to update incr_Minsup. cur_Minsup is calculated according to the following equation [50]:…”

Section: Complexitymentioning

confidence: 99%

“…where support (A ⟶ B) is the number of transactions containing itemsets A and B together, and support (A) is the count of transactions composing itemset A [50].…”

Section: Generation Of Interesting Rulesmentioning

confidence: 99%

An Incremental Interesting Maximal Frequent Itemset Mining Based on FP‐Growth Algorithm

Alsaeedi

Al-Hegami

2022

Complexity

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Frequent itemset mining is the most important step of association rule mining. It plays a very important role in incremental data environments. The massive volume of data creates an imminent need to design incremental algorithms for the maximal frequent itemset mining in order to handle incremental data over time. In this study, we propose an incremental maximal frequent itemset mining algorithms that integrate subjective interestingness criterion during the process of mining. The proposed framework is designed to deal with incremental data, which usually come at different times. It extends FP-Max algorithm, which is based on FP-Growth method by pushing interesting measures during maximal frequent itemset mining, and performs dynamic and early pruning to leave uninteresting frequent itemsets in order to avoid uninteresting rule generation. The framework was implemented and tested on public databases, and the results found are promising.

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“…FI is the empty set after a limited number of repetitions.Figure7depicts the Apriori algorithm's flow[19].Scan the database D, generate the candidate set C k and calculate the support degree of each candidate…”

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confidence: 99%

Design of teaching quality evaluation system for higher vocational education based on data mining technology

2023

Applied Mathematics and Nonlinear Sciences

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It is advantageous for schools to realize exact education management, the cornerstone for developing abilities, by constructing higher vocational education teaching quality evaluation systems. The division of frequent item sets based on the modified Apriori algorithm under the association rule algorithm is used for the evaluation algorithm of this system to examine the evaluation data in the evaluation system, which is created in this work using data mining technology. Data validation was carried out from system performance and application analysis of association rules for the teaching quality evaluation system constructed in this paper. Regarding system performance, the highest response time of the system in this paper is 8.34s, which is about 56.94% and 40.68% shorter than that of the evaluation system based on AHP and gray correlation theory, respectively. This shows that using association rule algorithms can realize the operation of the evaluation system faster, improve evaluation efficiency, and use visual data to achieve precise education management in schools.

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Optimization of Teaching Management System Based on Association Rules Algorithm

Cited by 6 publications

References 30 publications

Digitalization of university education management applicable to association rule mining algorithms

Digitalization of university education management applicable to association rule mining algorithms

An Incremental Interesting Maximal Frequent Itemset Mining Based on FP‐Growth Algorithm

Design of teaching quality evaluation system for higher vocational education based on data mining technology

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