A study on career planning and development decisions of university students based on improved association rule algorithm

Congcong Han

International Journal of Knowledge-Based Development, 2023, vol. 13, issue 2/3/4, 379-393

Abstract: The study is based on the Apriori algorithm in association rule algorithm to mine student information, and improves the algorithm by using Spark framework. The experimental results show that during the iterations, Algorithm 1 has the lowest MAE value, indicating that Algorithm 1 performs best in the career recommendation of students. After 600 iterations, the accuracy of Algorithm 1 reached 99.64%, which was 0.80% higher than Algorithm 2 and 1.11% higher than Algorithm 3. On the School 2 dataset, when the minimum support was set to 0.44, the running time of Algorithm 1 was 8.3 s. The running time of Algorithm 2 was 25.4 s, and the running time of Algorithm 3 was 273.7 s. The above results indicate that the improved Apriori algorithm proposed in the study is more efficient and accurate, and can effectively provide students with employment information recommendations, thus providing data support for students' career planning and development decisions.

Keywords: association rule algorithm; apriori algorithm; data mining; career planning; spark framework. (search for similar items in EconPapers)
Date: 2023
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