Machine Learning based on Association rules about automatic course data Mining

There is an intersection of knowledge between two different courses in automation major, and there is a knowledge graph between several courses. This paper takes nine courses of 127 undergraduate automation majors in a university in China as the research object. This paper attempts to find out the interrelationship and correlation intensity between courses, to guide students' self-study and teachers' lesson preparation. The research study employs the quantitative method of research. K-DD (Knowledge Discovery in Database) is used in this study. Relevance rules are used in this paper. First, we use the correlation coefficient method to calculate the correlation coefficient between the courses, and then use the correlation rule method to get the relationship between the nine courses. The algorithms in the association rules used in this paper are FP growth and Apriori algorithms. The correlation coefficient between automatic control theory and electronic measurement technology is up to 0.816. when the technical state of electronic measurement is true, the possibility that the automatic control theory is true is 0.867. When the automatic control theory state is true, the probability that the electronic measurement technology is true is 0.839. The correlation between automatic control theory and electronic measurement technology is the highest. The fact shows that there is indeed a strong correlation between the two courses.