# Analysis of University Students' Behavior Based on a Fusion K-Means Clustering Algorithm
## 4 Discussion  
Now we could analyze university students' behavior based on the results shown in Table 4 and Figure 12. From Table 4, it can be found the students' behaviors were different among four universities. S1 was taken as an example to analyze the students' behaviors. The center point of the first category was (6.0389, 5.47474), 6.0389 was the score of living habit, and 5.47474 was the score of learning performance, the center point indicates that the living habits and learning performance of students in this category were moderate. The center point of the second category was (8.01667, 3.18542), which indicates that students in this category had good living habits but poor learning performance. The fact was adverse in the third type, the center point of the third category was (4.15417, 7.85827), which shows these students performed well in learning performance and badly in living habit. Figure 12 shows that students in these four schools could be divided into seven categories, but the distribution of student behavior categories in different schools was different. From Figure 12, the distribution of most S1 students was located in the top right of the figure. Compared with the three other universities, it could be found that the students performed very differently between each category. Most of the S2 categories were in the lower right of the picture, which indicates that the learning performance was better, but the living habits were poor. S3 and S4 students performed moderately in living habit and learning performance. We measured and compared the effects of the three algorithms on the behavior clustering of university students by calculating SC, CHI, DBI, and SSE. It can be seen from Table 6 and Figures 16a, 17a, 18a and 19a that the SC and CHI of K-CFSFDP were higher than those of K-Means and CFSFDP on the four university data sets. It can be seen from Table 7 and Figures 16b, 17b, 18b and 19b that the DBI of K-CFSFDP was lower than that of K-Means and CFSFDP on the four university data sets. According to the SSE value, it could be clearly found that the K-CFSFDP algorithm was obviously better than the traditional K-Means clustering algorithm, and the partial SSE of the K-CFSFDP algorithm was less than CFSFDP algorithm. This shows that the clustering effect of K-CFSFDP was better than that of K-Means and CFSFDP, and could better cluster the behavior of university students. The experimental results in Tables 6 and 7 confirmed that because K-CFSFDP could determine the number of student categories (number of clusters) and student representatives (cluster centers), and used the Gaussian kernel function to calculate the point density, it has greater advantages. Therefore, compared with the other two algorithms, K-CFSFDP could better gather students with similar learning behaviors and living habits. Figure 19: Clustering performances of three models under different evaluation criteria in dataset S4: (**a**) the higher the value, the better and (**b**) the lower the value, the better. We compared the running time of the three algorithms based on the same data. From Table 8 and Figures 16, 17, 18 and 19, it could be found that the traditional K-Means algorithm ran faster than the other two algorithms. However, the time spent in its early stage was much longer than the other two algorithms. The running time of K-CFSFDP algorithm was shorter than CFSFDP. Therefore, K-CFSFDP could perform clustering faster and reduced the computing time of large-scale campus data. Compared with the current studies and methods on the behavior analysis of university students, the method proposed in this paper had considerable advantages in the following aspects:  
1. Traditional data mining and supervised machine learning methods often set labels in advance when classifying student behaviors. University student behavior data is often only used to analyze the relationship between behavior characteristics and labels.