The Evaluation of Ethnic Costume Courses based on FP-growth Algorithm

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Rui Xu


In order to make full use of the accumulated curriculum data of Folk costume and dig out useful information from it, so as to provide useful information for curriculum teaching, the article proposes three general functions based on the requirement analysis, and pre-processes the completed grade data of ethnic costume students in 4 academic years, analyzes these data by FP-growth algorithm to understand the situation of association rules between different courses, and through K-means++ algorithm The clustering analysis of students with different levels of achievement is carried out and the results are validated by examples. In the algorithm performance analysis, the performance of FP-growth algorithm is better, the average absolute error of FP-growth algorithm is always smaller than that of Apriori algorithm; When the support degree is 20%, the running time of FP-growth algorithm is 0.4s, which is 0.4s less than that of Apriori algorithm. when the number of calculation nodes is 5, the running time of FP-growth algorithm and the accuracy of the K-means++ algorithm were higher than that of the K-means algorithm. In the Iris dataset, the accuracy of the K-means++ algorithm was 91.05%, which was 8.94% higher than that of the K-means algorithm. When mining the course grade data, the confidence level of the obtained association rules was even higher, even up to 97.15%. The standardized test score for the second group of students was 0.960. The course evaluation method used in the article was more objective and the accuracy of the data analysis was higher, providing valuable reference information for teachers' teaching.


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Special Issue - Scalable Computing in Online and Blended Learning Environments: Challenges and Solutions