# Analysis of University Students' Behavior Based on a Fusion K-Means Clustering Algorithm
## 2 Materials and Methods
### Students' Behavior Data from Four Universities
#### 2.1.3 Data Visualization  
In order to provide more details of the data, this study used violin plots, box plots, scatter plots and scatter plot matrices to visualize the data. From the data visualization chart, we could get the distribution law and aggregation degree of the data. The violin plot can display the distribution status and probability density of multiple sets of data. The violin chart of the data is shown in Figure 1. Green represents the living performance score, blue represents the learning performance score, and the abscissa corresponds to 4 different universities. It can be seen from the violin chart that the distribution of student behavior data in different schools is different. The living data and learning data of students in the same school are not the same. In addition, it can be intuitively seen from the figure that the student behavior data does not meet the common normal distribution, and the data is concentrated near certain values, so there is a possibility of clustering. A box plot is a statistical chart that can display data dispersion. It can display the maximum, minimum, median, outlier, and upper and lower quartiles of a set of data. It can provide key information about the location and dispersion of data, especially when comparing different university student behavior data. The box diagram of the data is shown in Figure 2. Green represents the living performance score, blue represents the learning performance score, and the abscissa corresponds to 4 different universities. The horizontal line in the middle of the box is the median of the data, and the upper and lower ranges of the box correspond to the upper and lower quartiles of the data. The horizontal line above the box is the maximum value, and the horizontal line below the box is the minimum value. The solid points are outliers. It can be seen from the figure that, overall, the learning scores of college students in most schools were lower than living scores. In addition, the center points of most data were between 4 and 7. Figure 1: Violin plot of data from 4 universities. The data distribution scatter diagram is shown in Figure 3. It can be seen intuitively from the figure that the data points show the phenomenon of aggregation. Some data points are very densely distributed, while others are relatively sparsely distributed. We can see very intuitively from the data distribution map of the first university that these data points were clustered into 7 categories. This shows that many students had similar living habits and learning performance and could be divided into a certain number of categories. Further, we draw scatter plot matrices to comprehensively show the distribution of data and the shape of aggregation. The scatter plot matrix can reflect the correlation between learning scores  
Figure 3: Data distribution scatter plot of 4 universities. Figure 2: Box plot of data from 4 universities. and living scores. The matrix includes three types of charts. The upper triangle represents the scatter plot of the data, the diagonal line represents the probability density distribution map, and the lower triangle represents the contour map (the denser the data, the more concentrated the line, and the brighter the color). The scatter plot matrices of the four universities are shown in Figures 4-7 respectively. It can be seen from these scatter plot matrices that some data points were very densely distributed. This proves that some university students had similarities in living and study, and they gathered into a certain number of clusters. In addition, it can be seen from the figure that the number of distribution groups, the characteristics of study and life, and the degree of aggregation of student behavior in different universities were different. Therefore, the task of this research was to divide these categories using cluster analysis, so as to make a scientific and targeted classification of university student behavior.