Research on Data Mining of College Students’ Physical Health for Physical Education Reform

Abstract

The traditional data mining method of students’ physical health has some problems, such as low recall rate of data mining, long mining time, and poor mining accuracy. Therefore, this paper proposes a data mining method of college students’ physical health for physical education reform. Using association rules to construct the correspondence between the fitness test data, the fitness test data can be classified and the data training model can be built. The decision tree of data attribute was built, and the physical health data was segmented by the segmentation technology. The information entropy of health data was calculated by the decision tree, and the information gain of health data sample set was obtained. The C4.5 algorithm was used to improve the ID3 algorithm. The improved decision tree was used to obtain the physique data splitting attribute, and the information gain rate was obtained by the ID3 algorithm correction. The k -means algorithm is used to divide the data into clusters, according to which the physical health data mining of college students is realized. Experimental results show that the recall rate of the physical health data mining method proposed in this paper is as high as 96%, the data mining time is only 3 s, and the accuracy of data mining is as high as 98%, indicating that the method proposed in this paper can improve the physical health data mining effect.

The Effect of Jogging on the Physical Health of College Students Based on Intelligent Computing

“…Its solution is the optimal solution of the initial optimization problem [15]. An optimization algorithm can be used to solve c; the parameter b can be calculated according to the Karush-Kuhn-Tucker condition:…”

“…For different loss functions v(•), the optimization problem of formula (15) has different forms. Commonly used loss functions are Vapnik ε (insensitivity loss function), quadratic ε, and Huber.…”

Evaluation of the Teaching Effect in the Context of Collective Lesson Preparation

“…The educational reform is advancing continuously in major countries and has become an irreversible trend globally, however, the effect of this reform is not ideal and the success rate is unsatisfactory, and the reason behind it is that the reform fails to exert an influence on teachers [1][2][3][4][5][6]. Since the success rate of the experience learning of teachers has a great impact on educational reform [7][8][9][10][11], in national policy and teaching reform practice, the Collective Lesson Preparation (CLP) methodology has become a main approach of the experience learning of college teachers [12][13][14][15][16][17][18].…”