Optimization of Teaching Management System Based on Association Rules Algorithm | Zendy

Qing Niu | Zendy

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Optimization of Teaching Management System Based on Association Rules Algorithm

Author(s) -

Qing Niu

Publication year - 2021

Publication title -

complexity

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.447

H-Index - 61

eISSN - 1099-0526

pISSN - 1076-2787

DOI - 10.1155/2021/6688463

Subject(s) - association rule learning , apriori algorithm , computer science , database transaction , merge (version control) , data mining , partition (number theory) , gsp algorithm , partition problem , a priori and a posteriori , management system , algorithm , machine learning , database , information retrieval , mathematics , philosophy , epistemology , combinatorics , management , economics

The teaching management department carries all the work related to teaching in the whole school. A scientific, efficient, and complete teaching management system cannot only help the teaching management department improve work efficiency and quality but also greatly reduce many problems caused by manual labour risk. This paper designs and implements a teaching management system based on an improved association rule algorithm. First, aiming at the low efficiency of the Apriori algorithm for mining association rules, an association rule model based on interest is proposed. Second, use the MapReduce calculation model to partition the transaction database, then use the improved Apriori optimization algorithm for mining, and finally merge the mining results to obtain frequent itemsets. Through experiments, the optimized algorithm has greatly improved selection mining and computing time than traditional algorithms.

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