Visualization analysis of junior school students' pubertal timing and social adaptability using data mining approaches

Pubertal timing and social adaptability are important research contents of adolescent mental health education. Traditional research methods mainly classify students based on the total score or average score of the scale, although this kind of method is simple easy to conduct, it can't make a more detailed analysis of the students. In this paper, data mining methods such as association rules and clustering are used to analyze the data of pubertal timing and social adaptability scale, some novel and meaningful conclusions are figured out from the analysis results that can't be obtained by the previous methods, and the analysis results are visualized to enhance readability. Association rule mining on basic attributes information, the pubertal timing group and the social adaptability levels were performed which can explore the relationship between the basic attributes information of the students, pubertal timing and the social adaptability. Fine-grained analysis of social adaptability by using clustering method was conducted which can divide the similar students into the same groups that is very useful for teachers to have a more in-depth, accurate and detailed understanding of students, make sure that the better classification can be obtained compared with the traditional analysis approaches. The work of this paper provides an effective guidance and a novel perspective for how to use data mining technologies to study the pubertal timing and social adaptability problems.