Application of an Improved Sequence Pattern Association Rule Algorithm-based Data Management System for Continuing Education Teaching Data in Universities

A study proposes a university continuing education teaching data management system using an improved sequential pattern association rule algorithm. By introducing utility and interestingness parameters alongside support and confidence, the algorithm identifies efficient, engaging items. Experiments...

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Bibliographic Details
Published inApplied mathematics and nonlinear sciences Vol. 10; no. 1
Main Authors Peng, Hua, Yi, Chun
Format Journal Article
LanguageEnglish
Published Beirut Sciendo 01.01.2025
De Gruyter Poland
Subjects
97B90
algorithms
association rules
further education data
sequential pattern mining
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Summary:A study proposes a university continuing education teaching data management system using an improved sequential pattern association rule algorithm. By introducing utility and interestingness parameters alongside support and confidence, the algorithm identifies efficient, engaging items. Experiments show it reduces computing time and eliminates up to 45% of known association rules, enhancing timeliness, accuracy, and speed in college education data mining management.
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ISSN:2444-8656
2444-8656
DOI:10.2478/amns-2025-0826