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JACIII Vol.23 No.4 pp. 775-781
doi: 10.20965/jaciii.2019.p0775
(2019)

Paper:

Mining Association Rules on Enrollment Information of Higher Vocational Colleges Using the Apriori Algorithm

Tao Li

Qingdao Vocational and Technical College of Hotel Management
Qingdao, Shandong 266100, China

Received:
September 20, 2018
Accepted:
March 18, 2019
Published:
July 20, 2019
Keywords:
association rules, data mining, Apriori algorithm, admission information
Abstract

The enrollment work of higher vocational colleges is an important part of a school’s strategic decision-making. Developing a reasonable enrollment plan is highly important for a school’s development. Previous enrollment information contains extensive valuable information, which should be used by adopting effective methods of data processing. This study used an improved Apriori algorithm to mine the association rules of enrollment information to obtain the factors that affect enrollment. A higher vocational college in Qingdao was taken as the object of study. Three attributes were selected for association rule mining: college entrance exam results, applied majors, and student background. It was found that student registration rates were significantly different under different rules. The data mining results can provide policy support for future enrollment plans.

Cite this article as:
T. Li, “Mining Association Rules on Enrollment Information of Higher Vocational Colleges Using the Apriori Algorithm,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.4, pp. 775-781, 2019.
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Last updated on Sep. 19, 2019